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Data Transformation

What it is, types, techniques, and process. This guide provides a definition and practical advice to help you understand and practice data transformation.

DATA TRANSFORMATION GUIDE

What is data transformation.

Data Transformation refers to the process of converting the format or structure of a data set to match that of a target system. It involves cleaning, validating, and preparing data and is a critical stage of both the ETL and ELT processes. Data analysts, data engineers, and data scientists collaborate to execute these transformations for data integration, migration, and warehousing, and to turn data into a resource ready for analysis, insights and model training.

Data Transformation Process

The process of data transformation involves several key steps, which may vary based on your specific context and requirements. Plus, these workflows can be handled manually, automated or a combination of both. Here are the most common steps:

Data Discovery and Interpretation

Understand the types of data you currently have from different sources and determine what format it needs to be transformed into.

Consider file extensions, but also look deeper into the actual data structure to avoid assumptions.

Identify the target format for the transformed data.

Pre-Translation Data Quality Check

Before proceeding, verify the quality of your source data .

Detect missing or corrupt values that could cause issues during subsequent transformation steps.

Data Mapping and Code Development

Map the source data fields to their corresponding fields in the target format.

Use tools or develop scripts to perform the necessary transformations.

Code Execution and Validation

Execute the transformation code or processes (e.g., aggregations , calculations, filtering) to align the data with the desired format.

Validate the transformed data and data model to ensure correctness and consistency.

Address any discrepancies or errors that arise during this step.

Data Review and Documentation

Review the transformed data to confirm its accuracy and completeness.

Document the transformation process, including details about applied rules, mappings, and any adjustments made.

Maintain clear records for future reference and troubleshooting.

Keep in mind that not all your data will require transformation, on rare occasions, your source data can be used as is.

Types of Data Transformation

Various data transformation methods are employed to prepare raw data for data wrangling , analysis and model training. It’s important to note that not all techniques are universally applicable, and sometimes you may combine multiple methods.

Top transformation techniques:

Cleaning and Filtering: Identify inconsistencies, errors, and missing values in the data set. Remove irrelevant or duplicate data, and handle missing values appropriately (e.g., by imputation or removal).

Data Normalization: Scale numerical features to a common range (e.g., 0 to 1 or -1 to 1). This ensures consistent magnitudes across features, aiding machine learning models in accurate predictions.

Data Validation: Ensure data correctness by verifying data types, formats, accuracy, consistency, and uniqueness. It’s a crucial step to prevent flawed results in data analysis and machine learning.

Format Conversion: Change the representation of data from one format to another (e.g., encoding conversion, file format conversion, or data serialization). This brings compatibility across systems and applications, facilitating seamless data exchange and efficient processing.

Power Transform: Use mathematical techniques to stabilize variance and make data distributions more Gaussian-like, which is essential for improving the performance of machine learning algorithms. They include methods like the Box-Cox transform and the Yeo-Johnson transform.

Attribute Construction: Create new features by combining or transforming existing ones. For example, calculating ratios, aggregating data, or deriving time-based features.

Derivation: Similar to attribute construction, here you create new variables or columns based on existing data through calculations or transformations.

Encoding Categorical Variables: Convert categorical variables (like gender or product categories) into numerical representations (one-hot encoding, label encoding, etc.). This is sometimes referred to as vectorization.

Log Transformation: Apply logarithmic transformation to skewed data distributions to make them more symmetric.

Smoothing: Reduce noise in time series data by applying moving averages or exponential smoothing.

Aggregation: Summarize data at a higher level (e.g., daily sales aggregated to monthly sales).

Discretization: Convert continuous variables into discrete bins (e.g., age groups, income brackets).

Feature Scaling: Standardize features to have zero mean and unit variance (e.g., using Z-score normalization).

Feature Engineering: Create new features based on domain knowledge or insights from the data.

Key Structuring: Map specific meanings to generic identifiers for use as unique keys.

Data Enrichment: Enhance the dataset with additional information from external sources to provide more context or detail for analysis.

Simple Manipulations: Enhance search performance by sorting, ordering and indexing data. Pivoting converts column values into rows or vice versa.

Transformations in ETL and ELT

First, let’s define the letters ETL:

Extract refers to the process of pulling data from a source such as an SQL or NoSQL database, an XML file or a cloud platform.

Transform refers to the process of converting the format or structure of a data set to match that of a target system.

Load refers to the process of placing a data set into a target system.

Now we’ll describe how data transformation can occur at different points in your data pipeline depending on whether you have on-site or cloud-based storage and the size of your data sets.

ETL: On-Premises Storage & Small Data Sets

If you have traditional on-premises data storage and you have small data sets which require complex transformations, you’d follow an extract, transform, load (ETL) process, where transformation happens during the middle “transform” step.

A predetermined subset of data is extracted from the data source.

Data is transformed in a staging area in some way such as data mapping, applying concatenations or calculations. Transforming the data before it is loaded is necessary to deal with the constraints of traditional data warehouses.

Data is loaded into the target data warehouse system and is ready to be analyzed by BI or data analytics tools.

Key advantages of ETL process :

Stability and Speed: ETL ensures stable and faster data analysis for specific use cases. Since the data is already structured and transformed, users can work efficiently.

Compliance Ease: ETL tools simplify compliance with standards like GDPR, HIPAA, and CCPA. Users can exclude sensitive data before loading it into the target system.

ELT: Cloud Storage & Larger Data Sets

All data is extracted from the data sources.

All data is immediately loaded into the target system (data warehousing or a data mart or data lake). This can include raw, semi-structured and structured data types.

Data is transformed in the target system and is ready to be analyzed by BI tools or data analytics tools.

Key advantages of ELT process :

Real-Time, Flexible Data Analysis: ELT allows users to explore the entire data set, including real-time data, in any direction without waiting for IT to extract, transform, and load additional data.

Cost Efficiency and Low Maintenance: ELT benefits from cloud-based platforms with lower costs and various storage and processing options. Additionally, the process requires minimal maintenance since data is always available, and transformations are typically automated and cloud-based.

Here are examples of how you might apply 3 types of data transformation in your overall data management process.

Data Aggregation. You may want to summarize information at a higher level, such as:

Calculating your average monthly sales from daily sales data.

Summarizing website traffic by week instead of daily records.

Aggregating customer purchase history to analyze overall trends.

Converting Non-Numeric Features into Numeric. Your machine learning algorithm may require numeric input. If you have non-numeric features (e.g., categorical variables), you can transform them into a suitable numeric representation. For instance, converting text labels into one-hot encoded vectors allows you to perform matrix operations on them.

Resizing Inputs to a Fixed Size. Certain models, such as linear regression or feed-forward neural networks, expect a fixed number of input nodes. If your input data varies in size (e.g., images with different dimensions), you can resize or crop them to a consistent size before feeding them into the model.

Benefits of Data Transformation

Data transformation offers many benefits that enhance the effectiveness of your data analysis and decision-making. Here are the 4 key benefits:

Improved Data Quality

Transforming data ensures that it is properly formatted, validated, and organized.

High-quality data protects applications from issues like null values, unexpected duplicates, incorrect indexing, and incompatible formats.

Enhanced Accessibility

Properly transformed data becomes more accessible to both computers and end-users.

It facilitates compatibility between different applications, systems, and types of data.

Efficient Decision-Making

By making data usable for analysis and visualization, transformation supports business intelligence and data-driven decision-making.

Until raw data is transformed, its true value cannot be fully leveraged.

Time and Cost Savings

Automating transformation processes reduces manual effort and saves time.

Companies can streamline data entry and analysis, leading to operational efficiency and cost reduction.

A robust data transformation process is complicated. Performing the techniques described above isn’t easy if you don’t have the right tools or full support of your organization. Here are some the key challenges you may face:

Growing Complexity

As unstructured data (such as text and media) explodes, the complexity of required transformations skyrockets.

Handling diverse, multi-structured data demands intricate logic and adaptability.

Ensuring Data Quality

Generating clean, consistent data post-transformation is critical but tricky.

Ensuring that transformed data meets quality standards (free from errors, duplicates, and inconsistencies) remains a challenge.

Performance Overheads

Transforming big data strains infrastructure and computational resources.

Heavy computational power is needed, which can slow down other programs or processes.

Securing Data

During transformation, sensitive information may be exposed.

Ensuring data security, especially when handling personally identifiable information (PII), is a constant challenge.

Tool Selection

Choosing the right data transformation tools is crucial.

Factors like cost, scalability, ease of use, and compatibility with existing systems impact tool selection.

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Data Transformation: A Total Beginner’s Guide

When starting your career in data analytics or data science, you’ll find a lot of companies rely on many sources of data. Sometimes the data sources are stored in different formats or technologies. For example, the company I work for uses both SQL and NoSQL solutions making it difficult to join the raw data together. 

Using data transformation techniques, it’s possible to bring the data out of their silos, reformat them and store them in a central location called a data warehouse. 

In this data transformation tutorial, we will simulate working with SQL and NoSQL data by walking through the steps of transforming JSON data into tabular data in SQL Server. By the end of this article you’ll have learned the following about data transformation:

• What is data transformation?
• How does data transformation work?
• How to perform a data transformation
• The benefits of data transformation
• The challenges of data transformation

Feel free to use the clickable menu to skip ahead to any section. With that said, let’s get started!

1. What is data transformation?

At a high level, data transformation is the operations by which source data are formatted or reshaped to fit the constraints of downstream systems or processes. Data transformation is often considered a data engineering or data governance task, typically occurring in extract, transform, load or extract, load, transform (ETL/ELT) pipelines designed to process raw data.

These data transformation processes take extracted source data and add to it, delete from it, or format it before storing it. In large scale systems, data transformation is usually automated by software used for creating data warehouses and data lakes. However, on smaller scales, data analysts and data scientists oftentimes will need to perform data transformations manually so they can model the data to assist with data-driven decision making. 

An explosion in the Internet of Things (IoT) or “smart” devices has resulted in an age of big data . The massive increase in data means it is more important than ever to efficiently process and store data in ways that make it easy to analyze. By creating pipelines and processes to transform their data, organizations ensure they’re able to extract insights. 

To help illustrate data transformation in the ETL/ELT processes, we’re going to work through an example. Imagine you’re an analyst at a company that stores structured data via rows and columns in one database and unstructured data via JSON in another database.

In order to perform an analysis, you need to format the unstructured data so it can be joined with the structured data. Whether your process is based around ETL or ELT concepts, the transformation needs to take place so the raw, unstructured data ends up being compatible with the structured data. 

Next, let’s look at how data transformation works.

2. How does data transformation work?

Data transformation is a process that involves understanding the data, mapping the data to a destination system, and running the processes to perform the transformation. Before performing data transformation,  pre-processing the data might be required. Preprocessing data includes tasks like de-duplicating records, managing outliers, and imputing missing values. Preprocessing functions can be included in a data transformation pipeline.

The data transformation process

To better understand how data transformation works, let’s review the data transformation process by breaking it down into four steps:

• Discovering
• Performing 

We’ll go over each step in more detail now.

1. Discovering variables in the source data

Through data discovery, you need to identify variables of interest within the source data and figure out what pre-processing actions need to be performed to facilitate the data transformation. 

2. Planning the data transformation

To map the source data to its landing system, you need to determine the structure it needs to be in. In our example, we’ll convert our JSON data to a tabular format of rows and columns. In addition to structure, in this step you’ll decide whether fields need to be renamed, dropped, or aggregated. 

3. Performing the data transformation

Several tools or programming languages can be used to perform the data transformation. For example, Microsoft Excel remains one of the most popular tools in data analytics and has several functions and features that can transform data within the spreadsheet. When working with big data, Python and SQL are popular programming languages for data transformation. In our example, we’ll use SQL.

Related watching: What are the key languages data analysts use?

4. Reviewing the data transformation

Once the data transformation has occurred, evaluate it to make sure the results are as expected. For example, use tools to count records or verify duplicates have been removed, or that data aggregations have been performed correctly. 

Now that you know all of the steps involved in data transformation, let’s get on with a short tutorial!

3. How to perform a data transformation: An example using SQL

Now that we’ve reviewed how to transform data using the 4-step process, let’s apply the steps using real data, transforming JSON data into tabular data using SQL. Databases relying on SQL have remained some of the most important tools for data management over the last 40 years, so understanding SQL is very important in the field of data analytics. 

In this example, I’ll be using Microsoft SQL Server and SQL Server Management Studio . Both can be downloaded for free. If you’re new to SQL, check out this beginner tutorial .

Step 1: Create the source JSON data

JavaScript Object Notation (JSON) is one of the most popular data interchange formats, so there is a good chance you’ll encounter it when working with data and APIs. The JSON format supports hierarchical data structures, making use of objects and arrays. An object is a key/value pair contained within curly brackets {}. For example:

{“key1” : “value1”, “key2”:”value2″}

An array is a list of values contained within brackets []:

[“value1”, “value2”, “value3”]

To keep things simple, we’ll save some JSON data within the same database as our destination table. In the real world, the JSON data could be in a different database or streaming from a completely different system.

Create a database named dataTransformation using the following code:

Create a new table that stores the source JSON data named sourceJSONTable .

Then, populate sourceJSONTable using the following INSERT script:

Next, you’ll verify the JSON data has been inserted into the sourceJSONTable.

Step 2. Create the destination table

Now that we’ve got JSON data in sourceJSONTable, let’s create our destination table. After creating the table, we’ll go through the four steps for how to transform data (discovering, planning, performing, reviewing).

Create the destination table using the following INSERT script:

Step 3. Discover data

In the first step of data transformation, we inspect our source data to identify the variables of interest. Comparing the source data to the destination table, we see our variables of interest are country , state , abbreviation , and city . Looking at the JSON data, we can see our variables of interest are stored in key:value pairs, along with a nested JSON array. 

Step 4. Plan data transformation

In the discovery step we identified our variables of interest, now let’s put together a plan to transform the data. We need to pull out each object in the JSON and insert it into a separate column in the destination table.

This is a data transformation technique called flattening since we’re transforming the hierarchical JSON data into a non-hierarchical structure. SQL Server has a function called OPENJSON that can be used to flatten JSON. A real data transformation plan might look something like this:

Step 5. Perform the data transformation

We can write a simple SQL script that utilizes the OPENJSON function in SQL Server to perform the data transformation. The OPENJSON function flattens each of the JSON hierarchies in our source data. 

Notice the WHERE clause filters the data based on the state and abbreviation relationship. That’s because the cross apply causes the records to join all the other records in a many-to- many relationship, duplicating our state data. For example, without filtering in the WHERE clause, our data would look like this:

6. Review the data transformation

After executing the INSERT script, select data from the destination table to verify the records look correct.

As expected, we see our three rows in the destination table. And there you have it—that’s how to transform data using the 4-step process we outlined earlier: discovering, planning, performing, reviewing .

4. Benefits of data transformation

The biggest benefit of transforming data is that it makes data easier to work with by improving consistency and data quality. In general, data plays an important role in organizations, allowing them to understand their users, audit financials, revise internal processes, identify trends, and make forecasts and predictions.

Even with all of their data, unless they utilize data transformations to join, aggregate, consolidate, or enrich their data, gaining insight from it could be impossible. Consider the following ways data transformation benefits organizations:

Improved data quality

The data transformation pipeline cleans up things like duplicates, null values, and outliers. We also make sure the structure of the data is transformed to match the destination system so it can be queried and accessed the same way. Beyond cleaning and restructuring, data transformation can also include aggregating and feature engineering. Ultimately, improving data quality through  data transformation makes modeling the data much easier.

Better data accessibility

Transforming data so it can be stored in the same place overcomes one of the biggest obstacles in data analytics: Breaking down data silos. Organizations tend to keep data in silos. For example, customer data could be in one database while product event logs are in another, and sales data in yet another. Data transformation makes it possible to store all the records in one place, in the same format.

Simpler data governance

Since data can be generated from many sources and stored in many silos, managing data can be very challenging. Data transformation can be used to generate metadata to help organizations keep track of which data are sensitive and need to be regulated. Good metadata makes data easier to manage.

5. Challenges of data transformation

There are many challenges that come with trying to transform data. Working with big data can be very resource intensive and expensive because it takes a lot of processing power and computation to transform billions of records. Data transformation also requires both a lot of domain knowledge, and a lot of expertise with the underlying technologies used in the ETL/ELT pipelines.

Compute-resource intensive

Big data means powerful resources are required to transform it. If you don’t have powerful hardware handling the data transformation pipeline, the systems can run out of memory or be too inefficient to keep up with all of the data. For example, I was running a data transformation on millions of records, joining data from one table with another.

The server I was using didn’t have enough RAM to complete the operation, so my process kept running into Out Of Memory errors. When these types of errors happen, it takes a lot of time and effort to re-plan and retry the data transformation.

Data transformation can be an expensive endeavor since it can require a lot of storage and a lot of expertise. ETL/ELT pipelines need to store the transformed data so it can be used for analysis. That means an organization needs a data warehouse in addition to the databases that store the raw data.

Beyond the costs of additional storage, Data engineers, analysts, and data scientists are well-paying, in-demand jobs. Organizations might not be able to afford many of them, leaving a small team with the burden of managing big data operations.

Requires domain knowledge 

As an experienced product analyst working in education technology for the past 10 years, I’m all too familiar with the challenges of transforming education data. For example, there are multiple calculations used to aggregate attendance data, or generate GPAs, or score standardized tests.

Without the domain knowledge, data transformation can result in errors and inconsistencies that lead to bad analysis and incorrect predictions. It can take a lot of time and effort to develop the domain knowledge necessary for effective data transformation.

As organizations capture bigger data from more and more sources, efficiently transforming data as a part of an ETL/ELT pipeline becomes necessary for data-driven decision making. Data transformation is the process by which raw data is made to work with downstream processes and is done in four steps: discovering, planning, performing, reviewing.

There are many benefits to transforming data, such as improving the data quality, enabling and empowering data analytics and data modeling processes, and improving data governance practices. Although data transformation improves an organization’s ability to make data-driven decisions, it can be very challenging to transform big data. Working with big data requires powerful computer hardware, lots of storage space, and expert-level domain knowledge.

Regardless of the challenges, data transformation remains an important aspect of data management, and it helps organizations get the most from their data. If this guide and tutorial to data transformation has piqued your interest in the field of data analytics, why not try out our free, 5-day introductory data analytics short course ?

You might also be interested in the following topics:

• The best SQL certifications for aspiring data analysts
• What is linear regression?
• A complete guide to time series analysis and forecasting

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The Essential Components of Digital Transformation

• Tomas Chamorro-Premuzic

It’s about so much more than your technology.

It’s problematic when companies decide to embark on a digital transformation agenda without having a clear definition, let alone vision, for what it means. The fundamental meaning of transformation is not about replacing old technologies with new ones, or capturing high volumes of data, or hiring an army of data scientists, or trying to copy some of the things Google or Amazon do. In fact, the essence of digital transformation is to become a data-driven organization, ensuring that key decisions, actions, and processes are strongly influenced by data-driven insights, rather than by human intuition. In other words, you will only transform when you have managed to change how people behave, and how things are done in your organization.

The digital revolution forced every organization to reinvent itself, or at least rethink how it goes about doing business. Most large companies have invested substantial cash in what is generally labelled “digital transformation.” While those investments are projected to top $6.8 trillion by 2023, they’re often made without seeing clear benefits or ROI. Although these failures have multiple causes, they are generally the result of underestimating the various steps or stages required to successfully execute a transformation agenda.

• Tomas Chamorro-Premuzic is the Chief Innovation Officer at ManpowerGroup, a professor of business psychology at University College London and at Columbia University, co-founder of  deepersignals.com , and an associate at Harvard’s Entrepreneurial Finance Lab. He is the author of  Why Do So Many Incompetent Men Become Leaders? (and How to Fix It ) , upon which his  TEDx talk  was based. His latest book is I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique.   Find him at  www.drtomas.com . drtcp

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Data transformation: the complete guide for effective data management

Data transformation is a cornerstone of modern analytics, converting raw data into actionable insights. why is it essential, and how can it be executed efficiently this article dives straight into revealing the significance of data transformation in data management and its best practices..

Here, you’ll find comprehensive guidance on transforming your data for better analysis, ensuring it’s clean, standardised, and insightful. Begin your journey towards expert data handling now.

Key takeaways

• Data transformation is a critical process that involves cleaning, filtering, aggregation, and converting data to facilitate analysis and decision-making, commonly implemented through ETL (Extract, Transform, Load) procedures.
• The process enhances data quality by standardising, reducing redundancy, and ensuring compatibility with analytical tools, playing a pivotal role in data analysis to enable data-driven business insights especially in cloud data warehouse settings.
• Data transformation faces challenges such as the high cost of specialist expertise and resource intensity but offers benefits like improved data quality, compatibility, and innovative data architectures; it involves stages such as discovery, mapping, execution, and review.

What is data transformation?

Data transformation involves converting data from one format or structure into another, often to make it more suitable for analysis or storage. This process can include:

• Data cleaning
• Data filtering
• Data aggregation
• Converting non-numeric features to numeric ones
• Resising inputs to a fixed size
• Adding, copying, and replicating data

In simple terms, data transformation is the process of converting, cleansing, and structuring data into a usable format for analysis and decision-making. Think of it as a ‘data refinery’, turning raw data into high-quality information that can fuel data-driven decision making and business intelligence .

The process of data integration can vary from manual to automated, with organisations choosing the method based on their specific needs and capabilities. A common approach involves the Extract/Transform/Load (ETL) sequence, which involves:

• Identifying data sources
• Gathering data from those sources
• Cleansing and transforming the data
• Moving the data to a single repository

Ultimately, the goal is to prepare data for consumption by an intended recipient, which may include various end-users or systems.

Why is data transformation important in data analysis?

Data transformation is not just a technical process; it’s a crucial step that enables businesses to fully leverage their data for analysis and insight generation.

Transforming data ensures that it is in a suitable format for analysis, helps maintain data quality, and makes the data compatible with various analytical tools and techniques. Essentially, it’s the process that turns raw data into meaningful information that can fuel business insights.

In today’s business environment, a data warehouse plays a critical role in data transformation. The ability to transform data (especially in the cloud) allows organisations to:

• Streamline their operations
• Gain access to insights more quickly
• Handle vast volumes of data from disparate sources
• Extract valuable insights
• Make faster, smarter decisions

Challenges and benefits of data transformation

While data transformation offers immense benefits, it’s not without its challenges.

On the positive side, data transformation is essential for:

• Enhancing the quality of data and improving its usability and accessibility,
• Providing greater compatibility by ensuring data from one system works harmoniously with data from other systems for combined use,
• Creating innovative data architectures resulting in faster data access and storage.

However, data transformation often incurs high costs due to the need for specialised expertise from data scientists, which can be expensive and divert attention from other tasks .

Resource intensiveness is a significant challenge, particularly in on-premises data warehouses, resulting in computational burden and potential slowdown of other operations.

Additionally, the process is prone to potential errors which may lead to inaccuracies and decreased confidence in the quality and reliability of data.

However, despite the risks, data is and will continue to be used in an increasing range of actions:

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Key stages of data transformation

The data transformation process, which can also be referred to as a data pipeline, consists of key stages including:

• Data discovery
• Data mapping

Each stage plays a crucial role in the overall success of the process. These stages are not isolated, but rather interconnected parts of a cohesive process that ensures the data is accurately transformed and ready for analysis.

Data Discovery

The journey of data transformation begins with data discovery. This initial stage serves as the foundation for the entire process, involving the identification and understanding of data in its original format. Data profiling tools are utilised during this stage to identify data in its original source format.

Understanding the original data formats is crucial for planning the necessary transformations for data management. The ability to effectively use data profiling tools in data discovery sets the foundation for successful data transformation.

It’s like a treasure hunt, where the treasure is the valuable insights hidden within the raw data, especially when dealing with unstructured data.

Data Mapping

Once the data has been discovered, the next step is data mapping. This is where the current data structure is analysed, and the required transformations are planned.

It involves determining how individual fields will be modified, joined, or aggregated through data aggregation and establishing the necessary modifications.

A visual representation of how data from different fields will be altered is created during the data mapping phase . Essentially, data mapping is like drawing a roadmap, guiding the transformation process by matching or connecting data elements from one resource to another .

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During execution, data is extracted from various sources, the planned transformations are applied, and the transformed data is delivered to the target system.

The types of transformations applied during execution can be:

• Constructive modifications
• Destructive modifications
• Aesthetic modifications
• Structural modifications

This is the stage where the rubber meets the road, and the actual transformation of the continuous data takes place, addressing any missing values in the process.

The final stage in the data transformation process is review. This critical stage assesses whether the transformed data fulfills the objectives in terms of data format conversion . It’s where the integrity and structure of the data are validated to ensure that the format matches the intended design, confirming data quality and utility.

The review stage underpins the overall success of data transformation efforts by safeguarding against potential errors and aligning the final dataset with the strategic goals of the process.

What are some common methods and techniques of data transformation?

Data transformation encompasses a wide range of data transformation techniques, each with its specific purpose. Techniques can be:

• Constructive, creating new attributes or enhancing existing ones
• Destructive, removing irrelevant data to streamline analysis
• Focusing on standardising values for better human understanding and data presentation
• Adjusting the dataset’s organisation and structure for optimisation purposes

Advanced techniques such as log transformation and attribute construction are used for specific purposes like data normalisation and facilitating the data mining process.

Common techniques encompass cleaning, normalisation, validation, format conversion, aggregation, discretisation, and can be broken into six categories:

• Standardisation
• Normalisation
• Aggregation
• Generalisation
• Attribute construction
• Concept hierarchy generation.

Can data transformation process improve data quality? How?

One of the most significant benefits of data transformation is its ability to enhance data quality. By standardising data formats, correcting errors, and reducing redundancy, data transformation enhances the accuracy and usability of the data.

Essentially, data transformation acts as a quality control mechanism for your data. It ensures that your data is:

• in a format that makes it easy to analyse and derive insights from

It’s like a data quality check, ensuring that the data you’re working with is the best it can be.

What tools are typically used for data transformation?

Various tools are used in the data transformation process, each with its unique strengths.

In cloud-based environments, data transformation can follow either an ELT process, which converts data as it is uploaded, or an ETL process, where transformation is a distinct phase.

Automated data transformation tools reduce the need for manual scripting, offering capabilities that streamline and simplify the transformation process.

Some popular data transformation tools include:

• Apache Spark
• Informatica PowerCenter
• Microsoft SQL Server Integration Services (SSIS)
• IBM InfoSphere DataStage

Best practices for successful data transformation

Effective data transformation requires more than just the right tools; it also involves following best practices.

Here are some key steps to consider:

• Implement data governance frameworks to ensure consistent data transformation practices and adherence to compliance standards.
• Promote a data-driven culture of continuous improvement by conducting regular reviews and updates to data transformation procedures.
• Use reliable and efficient data transformation tools to streamline the process and improve outcomes.

By following these data transformation methods and steps, you can enhance the effectiveness of your data transformation efforts.

Establishing multidisciplinary teams that collaborate and share best practices improves the overall data transformation strategy. Investing in scalable data transformation processes is required to handle increasing volumes and varieties of data efficiently.

Why do businesses need data transformation and how can Future Processing help with this?

Data is king in business, so the transformation of data is not a luxury; it’s a critical requirement. Businesses need data transformation to convert data into usable formats, uncover hidden patterns, and gain insights that can drive growth and make accurate predictions .

Moreover, as businesses grow and evolve, their data needs also change. Data transformation allows for the flexibility and adaptability required to keep up with these changes.

That’s where Future Processing comes in. With over 23 years of experience in data migration and modernisation , data consulting , and data solutions development , Future Processing can help businesses navigate the complexities of data transformation.

Whether you’re just starting your data transformation journey or looking to optimise your existing processes, Future Processing has the expertise and tools to help you succeed .

Frequently Asked Questions

How does data transformation differ from data preprocessing.

Data transformation is a subset of data preprocessing. While data preprocessing encompasses all the steps taken to make data ready for analysis – including data cleaning, transformation, and reduction – data transformation specifically refers to the conversion of data from one format or structure to another. This can involve tasks such as normalisation, aggregation, and encoding of categorical variables to facilitate analysis or storage in a new system.

What is the difference between ETL and ELT?

ETL (Extract, Transform, Load) transforms data before it enters the data warehouse, while ELT (Extract, Load, Transform) loads data first and then transforms it within the warehouse. ETL is ideal for complex transformations of smaller datasets, whereas ELT suits large datasets and takes advantage of powerful cloud-based data warehouses.

What are the best practices for documenting data transformation processes?

Documenting data transformation processes is essential for maintaining clarity, repeatability, and compliance. Best practices include: maintaining a detailed transformation log, using clear and consistent naming conventions, creating visual flowcharts or diagrams, including metadata descriptions, version control and regularly reviewing and updating documentation.

How can data transformation be optimised for large datasets?

Optimising data transformation for large datasets involves using scalable tools and infrastructure, such as distributed computing systems, which can process large volumes of data in parallel. Techniques like data partitioning and indexing can also improve performance. Additionally, automating repetitive tasks and employing efficient algorithms for data cleaning and transformation can significantly reduce processing time and resource consumption.

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• April 5, 2022

Data Transformation Explained

Shayaan Saiyed

• Data Engineering 101

The ever-growing volume of data empowers companies to enhance decisions and fasten outcomes. Data is an advantage, but only if the business can use it. So how do you structure and make data accessible for stakeholders to drive insights? The answer is data transformation.

Raw data is not valuable until we do the hard work of transforming it to a state where the business can use it. Data teams must first arrange and format data so that they can create dashboards, reports, and predictive models. In this article, we’ll cover the basics of data transformation to set the required foundation for delivering business value.

What Is Data Transformation?

Data transformation is the process of converting data from one format, structure, or set of values to another by way of joining, filtering, appending, or otherwise performing some sort of computation on the data. The data transformation process is managed within a data pipeline . More specifically, the T in the ETL or ELT pipelines stands for transformation. 

While data transformation is a relatively simple concept, in practice it can be quite nuanced. If companies have ingested their data, can’t they use that data to create business analytics and dashboards? Why would they need to change it? Simply put, that data is very rarely in the right format and structure to be useful or usable to the right parties.

First off, when data is ingested from an API, blob storage, data warehouse, or another source, you have no control over the format. Most often, the data will not be in the fitting format for your destination. Beyond standardizing the format, there are many steps required to get data to a state where you can work with it. Or even apply it to your use cases and derive its full benefit. For example, filter out bad data, perform data quality checks, and aggregate data downstream .

That’s what data transformation is: the process of making your data valuable, usable, and reusable. The goal is to keep data organized and make it more compatible and easier for humans and computers to use.

Most Common Data Transformation Functions

A data team has infinite computational power over the data. However, each transformation layer’s design needs to satisfy the ultimate business requirement. The most common data transformation functions include:

Extraction and Parsing

In the early phases, you’re reformatting data, extracting certain fields, parsing, and looking for specific values. A data pipeline process starts with ingesting data from a source, followed by copying the data to a destination. This transformation technique concentrates on modifying the format and structure of the data. The objective is to guarantee that the data is compatible with the target system.

Filtering and Mapping

Afterwards, you’re refining your data by filtering or mapping fields and values. For example, you may want to display low-activity users in a customer-facing application. Or, the state field in your source may show New York as “New York,” but the destination may store it as “NY”. 

Data Enrichment

This type of transformation involves bringing in data from another source and adding it to your data set. For instance, you may want to add user metadata to build a more detailed view of specific users. In this phase, enriching the data can often turn into its own form of data ingestion . This step highlights just how sophisticated data transformation can get. 

Cross-Record Correlation

This type of data transformation involves analytical-style operations, such as “count how many users during a particular time did x, y, or z.”  There’s also ‘correlation of events’. You may want to determine if activities are distinct user sessions by correlating one user’s activity with the previous one. Or correlating to the following session and looking at the duration of the time gap. The transformation that happens, in this case, is the ordering and clustering of events. 

Let’s dive into an example. Your data is being ingested in a different format than you generally like to work with. Let’s say a log format, with some JSON-structured objects thrown in. In this case, it’s mostly semi-structured text data, as is often the case when data is coming from a back-end system that is logging user activity. Once you start to do analytical-style operations on the data, you need to take it from JSON-compressed files to columnar structures. That involves taking the JSON data, decompressing it, and putting it into a column format. 

Another example is filtering out the data you’re not interested in. This filter isn’t simply based on individual users, but also on the larger groups of people using the data. Let’s say you’re looking for people who create, update, and delete operations. But you are less interested in other types of events. Filtering out the data for those groups is a type of transformation that hones and refines the data set to make it more useful—and accurate for the downstream workload.

How to Transform Your Data

Conceptually, think of data transformation as a bidirectional search. Or finding the shortest path between two points in a graph. You need to map your raw data to your business needs. Then, figure out how to efficiently traverse from both sides towards the middle.

Often, business teams toss requirements to the data team with a list of demands. Other times, data engineering teams look at their data and figure out what to do with it—unrelated to business goals. The real value lies in skillfully blending the two and understanding the context in which the data will be used. Why are people looking for this data set? What are they trying to extract from understanding it? What is the next natural follow-on question they might ask?

Understand Both the Business Needs and the Data

Once you understand the goals the business needs to achieve, only then you can take stock of what data you need to work with. Planning transformations has traditionally taken a waterfall-style approach involving meetings, whiteboards, and diagrams. This can lead to a lot of expensive, complex work. Instead, teams need to make iteration cheap, easy, and streamlined.

Pipelines should be built in minutes to incrementally move forward to meet new business use cases . That includes mapping out the fields, prototyping a query, sending it off to the processing cluster, running the transformations, and validating . Data teams need to understand contextually why the data matters, as much as how to transform it and work with it.

Be Aware of the Physical Limitations of Data Pipelines

As you start querying the data, it’s not uncommon to simply start to transform it as you go without a specific plan. However, we recommend starting by breaking the process down into bite-sized transformations. This makes it easier to maintain the data pipeline as user needs and business logic inevitably change. Make sure the pipeline is simple and understandable enough for stakeholders to come in and make changes, if necessary.

In addition, it is important to understand how the infrastructure that supports your data pipelines needs to scale. As you build your transformations, consider how efficient your logic is, so you don’t run into unexpected errors. For instance, “Out of Memory” errors. This becomes important when you go from processing 100k records in your staging pipelines to millions in production.

Avoid Prematurely Optimizing Your Transformation Logic

Frequently, teams have optimized their transformation logic, but it’s not very maintainable. For instance, avoid winding up with 1,000-line SQL queries with complex, nested sub-queries. This may optimize processing, but not maintenance and engineering efforts. Break down queries into small components and understand the input and output for easier debugging and alteration.

Concurrently, take care not to over-optimize. Especially if you are working with a small data set. Once you get larger data sets and a better understanding of them, you can incorporate sophisticated transformations, such as incremental data propagation or compound nested transforms. Only do performance transformations once they become necessary.

Benefits and Challenges of Data Transformation

There are challenges to transforming data: 

• Data transformation can become expensive, depending on the software and resources. 
• Data transformation processes can eat up resources, whether on-premises or cloud-based. 
• Lack of expertise can introduce problems during transformation, so data analysts, engineers or anyone dealing with data transformation needs to have subject-matter expertise, so they can accurately and properly curate data. 
• Enterprises sometimes perform unnecessary transformations—and once changes are made, data teams might have to change it back again to make the data usable.

Even so, transforming data also yields several benefits:

• Once data is transformed, it is organized and easier—sometimes only now possible—for both humans and computers to use.
• Properly formatted and validated data improves data quality and ensures that applications run properly without encountering pitfalls such as incompatible formats, duplicates, or incomplete values.  
• Data transformation streamlines interoperability among applications, systems, and types of data. 

Final Thoughts About Data Transformation and Next Steps

Data transformation can be a tricky and nuanced step, but with the right tools and process, your data pipelines can become much more valuable, faster. You’ll be able to streamline data pipelines, ensure data integrity, and organize and interpret data in a meaningful way for engineers and analysts alike across data teams.

With Ascend for data transformation , you can easily make data transformation fast and efficient. You can design your pipelines with declarative definitions that require 95% less code and result in far less maintenance and specify inputs, outputs, and data logic in multiple languages: SQL, Python, Scala, and Java specs. 

Ascend’s full-featured SDK lets you programmatically create and interact with Ascend components, integrate with code repositories such as Github, and build reusable components. All this helps teams avoid work that’s not essential to deriving business value from data.

With queryable pipelines, you can treat any stage of any data pipeline as a queryable table. You can quickly prototype new pipeline stages or run ad-hoc queries against existing pipeline stages, all in a matter of seconds. When underlying data has changed, you’re immediately notified.

Start transforming or schedule a demo today.

Additional Reading and Resources

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Data Transformation: Definition, Processes, and Use Cases

Discover the ins and outs of data transformation.

Data transformation refers to the process of converting data from one format or structure to another. It involves various processes and techniques to ensure that data is accurate, reliable, and suitable for specific purposes. In this article, we will explore the different aspects of data transformation, including its definition, processes, types, and use cases. We will also discuss the challenges associated with data transformation and how organizations can address them.

Understanding Data Transformation

Data transformation is an integral part of data management and analysis. It involves manipulating raw data to make it more meaningful and useful. By transforming data, organizations can enhance decision-making, improve operational efficiency, and gain valuable insights.

One key aspect of data transformation is the process of data enrichment. This involves enhancing the raw data with additional information from external sources to provide more context and depth. For example, enriching customer data with demographic information can help organizations tailor their marketing strategies more effectively.

The Basics of Data Transformation

Data transformation starts with a thorough understanding of the data sources and their formats. It is essential to identify the types of data and their characteristics. This includes understanding the data structures, relationships, and any limitations or constraints associated with the data.

Furthermore, data transformation often involves the use of advanced analytics techniques such as machine learning and artificial intelligence. These technologies can help uncover hidden patterns and trends in the data, providing valuable insights that may not be apparent through traditional analysis methods.

Once the data is well understood, it can be transformed using various techniques such as data cleaning, integration, and reduction. These processes ensure that the data is standardized, consistent, and accurate.

Why Data Transformation is Important

Data transformation is important for several reasons. Firstly, it helps align disparate data sources, allowing organizations to combine and integrate data from different systems. This enables a holistic view of the data, facilitating meaningful analysis and decision-making.

Secondly, data transformation improves data quality. By standardizing and cleaning the data, organizations can eliminate errors, inconsistencies, and duplicates. This ensures that the data is reliable and accurate, leading to better insights and more informed decisions.

Overall, data transformation plays a crucial role in unlocking the full potential of data assets within an organization. It empowers businesses to leverage their data effectively, driving innovation, improving competitiveness, and fostering growth.

Delving into the Processes of Data Transformation

Data transformation involves several processes that work together to convert raw data into meaningful information. Let's explore three key processes:

Data Cleaning

Data cleaning, also known as data scrubbing, involves identifying and correcting errors, inconsistencies, and outliers in the data. This process ensures that the data is reliable and accurate by addressing issues such as missing values, incorrect formatting, and duplicate records. Data cleaning plays a crucial role in enhancing data quality and integrity.

One important aspect of data cleaning is outlier detection. Outliers are data points that significantly differ from other observations in a dataset. Detecting and handling outliers is crucial as they can skew analysis results and lead to incorrect conclusions. Various statistical methods, such as z-score analysis or box plots, can be used to identify outliers and decide whether to remove or adjust them.

Data Integration

Data integration involves combining data from multiple sources to create a unified and consistent view. This process is essential when dealing with data stored in different formats or systems. By integrating data, organizations can overcome data silos and gain a comprehensive understanding of their information assets.

Another challenge in data integration is schema matching, where the attributes from different datasets need to be mapped to a common schema. This process requires careful consideration of data semantics, structure, and relationships to ensure accurate integration. Tools like schema matching algorithms and ontology-based approaches can assist in automating this matching process and reducing manual effort.

Data Reduction

Data reduction aims to remove redundant, irrelevant, or less important data to improve efficiency and reduce storage costs. This process involves applying techniques such as data aggregation, summarization, or sampling. By reducing the volume of data, organizations can focus on the most relevant information and extract valuable insights efficiently.

Dimensionality reduction is a common technique used in data reduction to address the curse of dimensionality. High-dimensional data can lead to increased computational complexity and decreased model performance. Methods like principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE) can be employed to reduce the number of dimensions while preserving essential information for analysis.

Different Types of Data Transformation

Data transformation can be classified into two main types: simple data transformation and complex data transformation.

When it comes to data transformation, there are various techniques and methods that can be employed to manipulate and process data effectively. Understanding the different types of data transformation is crucial for organizations looking to derive valuable insights and make informed decisions based on their data.

Simple Data Transformation

Simple data transformation involves basic operations such as changing data formats or structures, renaming columns, or applying simple calculations. These transformations are relatively straightforward and do not require advanced techniques or complex algorithms.

For example, in simple data transformation, a common task might involve converting a date field from one format to another, such as changing "MM/DD/YYYY" to "YYYY-MM-DD". This type of transformation is essential for ensuring data consistency and compatibility across different systems and applications.

Complex Data Transformation

Complex data transformation involves advanced techniques and algorithms to perform sophisticated operations on the data. This could include tasks such as data imputation, outlier detection, data normalization, or predictive analytics. Complex data transformation requires specialized skills and tools to manipulate and analyze the data effectively.

One of the key challenges in complex data transformation is dealing with missing or incomplete data, a common issue faced by organizations working with large datasets. Data imputation techniques, such as mean imputation or regression imputation, can be used to estimate missing values based on the available data. This process is crucial for maintaining data integrity and ensuring the accuracy of analytical models and insights derived from the data.

Use Cases of Data Transformation

Data transformation has various applications across different domains. Let's explore two common use cases:

Data Transformation in Business Intelligence

In business intelligence , data transformation plays a vital role in converting raw data into meaningful insights. By transforming data, organizations can create interactive dashboards, reports, and visualizations that enable stakeholders to make informed decisions. Data transformation also facilitates data integration from various sources, allowing businesses to gain a holistic view of their operations and market trends.

Moreover, data transformation in business intelligence can involve cleaning and standardizing data to ensure consistency and accuracy. This process often includes handling missing values, removing duplicates, and normalizing data formats. By performing these transformations, organizations can enhance the quality of their analytics and reporting, leading to more reliable business decisions.

Data Transformation in Machine Learning

Data transformation is essential in machine learning algorithms. Preprocessing and transforming data are critical steps in preparing datasets for model training. These transformations may include feature scaling, one-hot encoding, or dimensionality reduction. Proper data transformation ensures that the input data is in a suitable format for the machine learning algorithms, leading to more accurate predictions and improved model performance.

In addition to the mentioned transformations, data normalization is another crucial step in data preparation for machine learning. Normalizing data helps in bringing all features to a similar scale, preventing certain features from dominating the learning process due to their larger values. This normalization process is particularly important in algorithms sensitive to the scale of input data, such as support vector machines and k-nearest neighbors.

Challenges in Data Transformation

While data transformation offers numerous benefits, it also presents challenges that organizations need to address:

Data Quality Issues

Data transformation heavily relies on the quality of the input data. Poor data quality, such as incomplete or inconsistent data, can affect the output of the transformation processes. Organizations need to implement robust data governance practices and quality control measures to ensure the accuracy and reliability of the transformed data.

Scalability Concerns

As the volume and variety of data continue to grow exponentially, organizations face challenges in scaling their data transformation processes. Traditional methods may no longer be sufficient to handle large datasets efficiently. Organizations need to invest in scalable technologies and architectures, such as cloud-based solutions or distributed computing frameworks, to handle the increasing demands of data transformation.

Another significant challenge in data transformation is data integration. Organizations often deal with data from multiple sources, such as databases, applications, and external partners. Integrating these disparate data sources and formats into a unified structure for transformation can be complex and time-consuming. It requires careful planning, data mapping, and the use of integration tools to ensure seamless data flow and consistency.

Moreover, data security and privacy concerns add another layer of complexity to data transformation processes. With the increasing regulations around data protection and privacy, organizations need to ensure that sensitive information is handled securely during the transformation process. Implementing encryption, access controls, and data masking techniques are essential to safeguard data integrity and confidentiality.

In conclusion, data transformation is a crucial aspect of data management and analysis. It involves various processes, techniques, and types to convert raw data into meaningful information. By transforming data, organizations can improve data quality, drive better decision-making, and unlock valuable insights. However, addressing challenges such as data quality issues, scalability concerns, data integration complexities, and data security considerations is essential to ensure successful data transformation.

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Data Transformation

Data transformation is the process of converting and cleaning raw data from one data source to meet the requirements of its new location. also called data wrangling, transforming data is essential to ingestion workflows that feed data warehouses and modern data lakes..

Analytics projects may also use data transformation to prepare warehoused data for analysis.

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Data virtualization in the cloud era.

Data transformation is the essential process through which data pipelines convert the structure and format of data from a source into a destination’s structure and format. Given the fragmented nature of enterprise data storage, the only way to generate holistic insights that support data-driven decision-making is to make data easily accessible through transformation.

This guide will introduce the data transformation process, its benefits, and the role it plays in extract, transform, and load data pipelines.

What is data transformation?

Data transformation is the process of converting and cleaning raw data from one data source to meet the requirements of its new location. Also called data wrangling, transforming data is essential to ingestion workflows that feed data warehouses and modern data lakes. Analytics projects may also use data transformation to prepare warehoused data for analysis.

What is the difference between data transformation and data management?

Data management consists of the technologies and practices used to handle enterprise data, from ingestion through storage, use, and disposal.

Data transformation is one element of data management. As part of data integration workflows, various types of data transformation convert the source data into the formats and structures of the destination datasets.

Why is data transformation important?

Data transformation remains indispensable because enterprise data ecosystems remain stubbornly heterogeneous despite decades of centralization and standardization initiatives. Each application and storage system takes slightly different approaches to formatting and structuring data. Organizational format, structure, and quality variations occur as business domains and regional operations develop their own data systems.

Without data transformation, data analysts would have to fix these inconsistencies each time they tried to combine two data sources. This project-by-project approach consumes resources, risks variations between analyses, and makes decision-making less effective.

The process of transforming data from multiple sources to meet a single standard improves the efficiency of a company’s data analysis operations by delivering the following benefits:

Data quality improvement

Cleaning raw data values is a basic function of transformation processes. Missing values, duplicate data, outliers, and corrupted values are fixed or mitigated to improve data quality.

Data enrichment offers further quality improvements. Other sources may replace data missing from the original. Supplemental data sources can add context that improves downstream use cases.

Data consistency

Applying company-wide data standards boosts analysis productivity. Business intelligence projects usually draw on multiple sources. Data science relies on the aggregation of big datasets. Something as simple as competing date formats throws obstacles in users’ paths.

Ensuring consistency among the datasets in the company’s data warehouses reduces the time users spend preparing data so they can focus on generating insights.

Data transformation also makes data about the data more consistent. Standardizing the schema, metadata, and other data properties improves the navigability of data infrastructure and speeds discovery.

Data integration

Integrating an enterprise’s dispersed and varied data sources creates a central repository for systems and people to access in support of the business. Whether built upon a data lake or an on-premises or cloud data warehouse, this source system combines different sources into a holistic resource for generating business insights.

Data analysis

Data transformation promotes the democratization of data access. Combining disparate data sources is a data engineering challenge. Few users have the skills to clean and enhance data from relational database management systems, much less real-time clickstreams.

Ingesting raw data through transformation pipelines results in clean, consistent data values and metadata that any analyst can use. Data becomes more accessible, empowering more people to use data and creating a data-driven decision-making culture.

Data modeling

Data models are visualizations of data flows within an information system. These models characterize the system’s data sources, define the destination’s format and other data standards, and document the system’s transformation requirements.

A data model is an essential reference for all transformation processes. Data warehousing requires models to ensure the data pool meets quality and consistency standards.

Engineers also rely on data models when working with their customers to develop data projects. For example, they will work with data scientists to define how to transform data for machine learning algorithms.

Security and compliance

Data transformation is not limited to ingestion pipelines or the preparatory phases of analytics projects. Modifying data can improve the security of data moving between systems. This kind of transformation could be something as commonplace as encrypting data transfers. Or it could be a form of data aggregation that anonymizes personal data to comply with privacy regulations.

Storage optimization

Transformation lets data managers optimize the cost and performance of their storage infrastructure. Deduplication and compression help control the amount of storage a data warehouse uses. Using transformation pipelines to improve metadata quality and better index data produces more efficient queries.

Periodically running transformation pipelines that assign usage tiers to data allow data managers to move infrequently used data to lower-cost storage options while reserving expensive, high-performance storage to the most frequently accessed data.

What is an example of data transformation? Steps to data transformation

A modern data stack uses extract, transform, load (ELT) or extract, load, transform (ETL) data pipelines to ingest data from various sources into a data warehouse. The difference between the two is whether the data pipeline transforms the data before ( ELT ) or after ( ETL ) the data reaches its destination.

Although any generalization of ELT and ETL processes will never match specific use cases, a typical data transformation process follows these steps:

1. Data discovery

Given the number of data sources available to an enterprise, the first step is identifying the most suitable datasets for a project. The data discovery process involves identifying sources that may be relevant and understanding the types of data, schemas, structures, formats, and other characteristics.

2. Data mapping

Engineers then map the data sources to the destination. The project’s data model will have defined how the new dataset should be structured and formatted to support the project’s business outcomes. Data mapping determines what changes the pipeline will apply to table headers, formats, metadata, and other aspects of each source dataset.

3. Data extraction

With all the preparation complete, engineers design the pipeline’s extraction phase. Each data source may have a different process for exporting data. Some will use variations of SQL, while others will provide APIs that return formats such as XML or JSON.

Data extraction pipelines will use the appropriate method for each source to bring data into an interim storage location.

4. Transform

With the data in temporary storage, the pipeline will apply the transformations needed to map the incoming data to its destination. This is where bad or missing data gets cleaned up, normalization reduces redundancy in tables, and additional data enriches the final dataset.

While engineers will conduct manual reviews during pipeline development, automation is the only way to monitor the performance of high-volume pipelines. These systems will ensure that data moving from the source system through the pipeline’s data transformation tools is not inadvertently altered or corrupted.

Finally, the pipeline loads the ingested data into the data warehouse. The loading process could involve appending or replacing data in existing tables. In other scenarios, the load phase will create new tables. In either case, updating the destination’s data catalogs and indexes helps improve data discoverability and query performance.

Data transformation with Starburst

Starburst is a modern data lake analytics platform that unites disparate enterprise data sources within a virtual access layer. By leaving data at the source and providing a single point of access to enterprise data no matter where it lives, Starburst empowers data users and frees engineers from routine requests.

In particular, data teams can use Starburst’s integration with dbt Cloud in their pipeline development workflows to simplify data transformation. Starburst’s federated queries become the single interface dbt Cloud uses to combine data from multiple sources.

Discovery: Starburst has connectors to more than 50 enterprise data sources, including relational databases, data warehouses, and data lakes. These connectors provide type mapping, SQL support, and other features that let engineers explore any data source in the company.

Mapping: Using the information gathered in the previous stage, engineers can use dbt Cloud to map data sources to destinations.

Extraction: Starburst’s massively parallel query engine extracts data from the sources and delivers it to a temporary landing zone.

Transformation: Dbt Cloud then applies its transformations to the extracted data.

Review: Engineers use dbt Cloud to test and monitor pipeline performance.

Loading: Starburst’s query engine loads the transformed data from the landing zone to the destination data warehouse or data lake.

Once in its final location, the data is available to authorized users throughout the company. Dashboards become more effective by incorporating accurate and consistent multi-source data. Analysts become more productive when using their preferred SQL clients to run queries themselves. And data teams have more time for higher-value projects that require complex transformation pipelines.

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Data Transformation: Types, Process, Benefits & Definition

What is data transformation? What are the benefits of data transformation? What are the processes? Learn all this and more!

Matt Palmer

What is data transformation.

Data transformation refers to the process of converting, cleaning, and manipulating raw data into a structured format that is suitable for analysis or other data processing tasks.

The last few decades have seen a renaissance in data collection and processing—today’s data teams have more information at their disposal than ever before.

While this has led to a proliferation of data analytics and science, it’s presented a number of problems for engineers and business teams.

Raw data can be challenging to work with and difficult to filter. Often, the problem isn’t how to collect more data, but which data to store and analyze.

To curate appropriate, meaningful data and make it usable across multiple systems, businesses must leverage data transformation.

Key Takeaways

• Data transformation is the process of converting raw data into a structured, standardized format to enable better analysis and decision-making.
• Key steps in data transformation include discovering data, mapping modifications, extracting data, executing code to transform it, reviewing for correctness, and loading the output.
• Benefits of data transformation include improved data quality, speed, organization, and management, but it can be expensive and requires contextual awareness to avoid errors.

Understanding Data Transformation

Transformation may occur on the format, structure, or values of data. With regard to data analytics, transformation usually occurs after data is extracted or loaded (E T L/EL T ).

Data transformation increases the efficiency of analytic processes and enables data-driven decisions. Raw data is often difficult to analyze and too vast in quantity to derive meaningful insight, hence the need for clean, usable data .

During the transformation process, an analyst or engineer will determine the data structure. The most common types of data transformation are:

• Constructive: The data transformation process adds, copies, or replicates data.
• Destructive: The system deletes fields or records.
• Aesthetic: The transformation standardizes the data to meet requirements or parameters.
• Structural: The database is reorganized by renaming, moving, or combining columns.

In addition, a practitioner might also perform data mapping and store data within the appropriate database technology .

The Data Transformation Process

In a cloud data warehouse, the data transformation process most typically takes the form of ELT (Extract Load Transform) or ETL (Extract Transform Load) . With cloud storage costs becoming cheaper by the year, many teams opt for ELT— the difference being that all data is loaded in cloud storage, then transformed and added to a warehouse.

The transformation process generally follows 6 stages:

• Data Discovery: During the first stage, data teams work to understand and identify applicable raw data. By profiling data, analysts/engineers can better understand the transformations that need to occur.
• Data Mapping: During this phase, analysts determine how individual fields are modified, matched, filtered, joined, and aggregated.
• Data Extraction: During this phase, data is moved from a source system to a target system. Extraction may include structured data (databases) or unstructured data (event streaming, log files) sources.
• Code Generation and Execution: Once extracted and loaded, transformation needs to occur on the raw data to store it in a format appropriate for BI and analytic use. This is frequently accomplished by analytics engineers, who write SQL/Python to programmatically transform data. This code is executed daily/hourly to provide timely and appropriate analytic data.
• Review: Once implemented, code needs to be reviewed and checked to ensure a correct and appropriate implementation.
• Sending: The final step involves sending data to its target destination. The target might be a data warehouse or other database in a structured format.

These steps are meant to illustrate patterns of data transformation— no single “correct” transformation process exists. The right process is the one that works for your data team. That is to say, other bespoke operations might occur in a transformation.

For example, analysts may filter data by loading certain columns. Alternatively, they might enrich the data with names, geo-properties, etc. or dedupe and join data from multiple sources.

Related Article:

Data Transformation Types

There are two common approaches to data transformation in the cloud: scripting-/code-based tools and low-/no-code tools. Scripting tools are the de-facto standard, with the greatest amount of customization, flexibility, and control over how data is transformed. Nonetheless, low-code solutions have come a long way, specifically in the last few years. We’ll briefly discuss both options.

Scripting Tools

The most common data transformations occur using SQL or Python . At the simplest, these transformations might be stored in a repository and executed using some orchestrator. More commonly, platforms like dbt are used to orchestrate and order transformations using a combination of SQL/Python. These tools or systems often boil down to programmatically creating tables or transformations using some scripting language.

The Python Runner SDK is also useful for scripting and automation. Enabling remote interactions with schedules, jobs, and business functions has never been easier. Want to see Runner in action? Schedule a demo of the Python Runner SDK.

Low-/No-Code Tools

These data transformation tools are the easiest for non-technical users to utilize. They allow you to collect data from any cloud source and load it into your data warehouse using an interactive GUI. Over the past decade, many low-code solutions have proliferated.

Zuar Runner is an example of a product that has ETL/ELT capabilities, but also helps you manage data at every step in its journey. Runner can be hosted either on-premise or in the cloud and has code and no code options.

Data Transformation Techniques

There are several data transformation techniques that can help structure and clean up the data before analysis or storage in a data warehouse. Here are some of the more common methods:

• Smoothing: This is the data transformation process of removing distorted or meaningless data from the dataset. It also detects minor modifications to the data to identify specific patterns or trends.
• Aggregation: Data aggregation collects raw data from multiple sources and stores it in a single format for accurate analysis and reports. This technique is necessary when your business collects high volumes of data.
• Discretization: This data transformation technique creates interval labels in continuous data to improve efficiency and easier analysis. The process utilizes decision tree algorithms to transform a large dataset into compact categorical data.
• Generalization: Utilizing concept hierarchies, generalization converts low-level attributes to high-level, creating a clear data snapshot.
• Attribute Construction: This technique allows a dataset to be organized by creating new attributes from an existing set.
• Normalization: Normalization transforms the data so that the attributes stay within a specified range for more efficient extraction and data mining applications.
• Manipulation: Manipulation is the process of changing or altering data to make it more readable and organized. Data manipulation tools help identify patterns in the data and transform it into a usable form to generate insight.

Data Transformation: Benefits and Challenges

Data transformation offers several benefits and challenges in the realm of data management.

Data Transformation Benefits

Transforming data can help businesses in a variety of ways. Here are some of the biggest benefits:

• Better Organization: Transformed data is easier for both humans and computers to use. The process of transformation involves assessing and altering data to optimize storage and discoverability.
• Improved Data Quality: Bad data poses a number of risks. Data transformation can help your organization eliminate quality issues and reduce the possibility of misinterpretation.
• Faster Queries: By standardizing data and storing it properly in a warehouse, query speed and BI tooling can be optimized— resulting in lower friction to analysis.
• Simpler Data Management: A large part of data transformation is metadata and lineage tracking. By implementing these techniques, teams can drastically simplify data management. This is especially important as organizations grow and demand data from a large number of sources.
• Broader Use: Transformation makes it easier to get the most out of your data by standardizing and making it more usable.

Data Transformation Challenges

While the methods of data transformation come with numerous benefits, it’s important to understand that a few potential drawbacks exist.

• Transformation can be expensive and resource-intensive : While processing and compute costs have fallen in recent years, it’s not uncommon to hear stories of extreme AWS, GCP, or Databricks bills. Furthermore, the resource cost from a man-hour/salary perspective is hefty: most companies require a team of data analysts /engineers/scientists to extract value from data.
• Contextual awareness is crucial: If analysts/engineers transforming data lack business context or understanding, extreme errors are possible. While data observability tooling continues to improve, there are some errors that are almost undetectable and could lead to misinterpreting data or making an incorrect business decision.

Nonetheless, data transformation is an essential part of any data-driven organization. Implementing tests and following the best-practices of software development will help to minimize errors and improve confidence in data.

Without experienced data analysts with the right subject matter expertise, problems may occur during the data transformation process. While the benefits of data transformation outweigh the drawbacks, it's necessary to take appropriate caution to ensure sound transformation.

Data Transformation Implementation

Organizing, transforming, and structuring data can be an overwhelming task for many organizations, but with the right research and planning it's possible to integrate a data-driven culture into your business.

And that's where Zuar Runner shines. With its end-to-end ELT solution, Zuar Runner eliminates the need for connecting multiple pipeline tools, saving both time and money.

By automating workflows, from raw data extraction to end-user visualization, data teams can reclaim their time and focus on implementing advanced analytics.

Here's a case study for Manduka, a retail company offering yoga equipment and apparel:

Zuar Runner's pre-built connectors and flexibility to add custom data sources provide effortless connectivity to almost any data format. With Zuar Runner, data transformation becomes a seamless and cost-effective process, enabling organizations to deploy enterprise-level tools without the hefty price tag.

Stop wasting time on manual data transformation and embrace the power of Zuar Runner to revolutionize your data pipeline and unlock the true potential of your data. Talk with one of our data experts to learn more and set up a demo!

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Data Transformation

Data Transformation can be defined as a process of converting data into a format that aids in building efficient ML models and deriving better insights. Data Transformation is an important step in the feature engineering and data processing stage of a Data Science project .

What is Data Transformation?

There is no doubt that in today’s world, data is the most important asset for organizations worldwide. The majority of data is raw, which makes it challenging to work with as it is difficult to understand this data directly. Therefore, it is essential to convert this data into a format that is more usable and understandable. Data Transformation is a technique used to transform raw data into a more appropriate format that enables efficient data mining and model building.

When to Transform Data?

• Data Transformation is an essential technique that must be used before Data Mining so that it can help in extracting meaningful patterns and insights.
• It is also essential to perform before training and developing an ML model. While training an ML model, both datasets used in the training and testing phase of the model need to be transformed in the same way.

Benefits and Challenges of Data Transformation

A few of the benefits companies get by using Data Transformation include as following :

Maximize Value of Data: Data Transformation standardizes data from various data sources to increase its usability and accessibility. This will ensure that maximum data is used in Data Mining and model building , resulting in extracting maximum value from data.

Effective Data Management: Data Transformation helps remove inconsistencies in the data by applying various techniques so that it is easier to understand and retrieve data.

Better Model Building and Insights: Typically, the distribution of features in a dataset is highly skewed. So, Data Transformation helps remove bias in the model by standardizing and normalizing features in the same range.

Improve Data Quality: Data Transformation helps organizations improve data quality by handling missing values and other inconsistencies.

Data Transformation comes with its own challenges as well. Let’s have a look at some of the challenges of the Data Transformation process.

Data Transformation is an expensive and resource-intensive process. This cost depends upon many factors such as infrastructure, tools, company requirements, data size, etc.

Data Transformation requires professionals with appropriate subject matter expertise as faulty Data Transformation can lead to inaccurate business insights.

Data Transformation Techniques

A few of the most common Data Transformation techniques include as following :

Data Smoothing

Data Smoothing is used to remove noise in the data, and it helps inherent patterns to stand out. Therefore, Data Smoothing can help in predicting trends or future events. For example, as shown in the below diagram, smoothing allows us to remove noise from the input data that helps identify implicit seasonality and growth trends. Some of the ways to perform Data Smoothing are moving average, exponential average, random walk, regression, binning, etc.

Attribute Construction

In this method, new attributes or features are created out of the existing features. It simplifies the data and makes data mining more efficient. For example, if we have height and weight features in the data, we can create a new attribute, BMI , using these two features.

Data Aggregation

Data Aggregation is the process of compiling large volumes of data and transforming it into an organized and summarized format that is more consumable and comprehensive. Data Aggregation can enable the capability to forecast future trends and aid in predictive analysis. For example, a company may look at monthly sales data of a product instead of raw sales data to understand its performance better and forecast future sales.

Ready to Dive Deeper? Explore the Practical Applications of These Concepts in Our Data Science Course and Turn Knowledge into Expertise.

Data Normalization

The range of values for each attribute in a dataset can vary greatly. Some features might contain large numbers, such as sales data, etc., while others might have comparatively smaller matters, such as age, etc. This could introduce a bias in the model building. Therefore it is essential to normalize every feature in the dataset. Data Normalization is a technique that is used to convert a numeric variable into a specified range such as [-1,1] , [0,1] , etc. A few of the most common approaches to performing normalization include :

Min-Max Normalization: This is a linear transformation and will convert the data into the [0,1] range. The formula for Min-Max Normalization is :

Z-Score Normalization: It utilizes the mean and standard deviation of the attribute to normalize it. It will ensure that the attribute has a 0 mean and 1 standard deviation. Z-Score Normalization is also called Data Standardization or Data Scaling . The below formula is used to perform Z-Score Normalization :

Data Discretization

It is a process of converting numerical or continuous variables into a set of intervals. This makes data easy to analyze and understand. For example, the age features can be converted into intervals such as ( 0-10 , 11-20 , ..) or (child, young, …).

Log Transformation

When input data does not conform to the normal distribution and has a skewed distribution, then Log transformation is used to transform/convert it into a normal distribution.

Reciprocal Transformation

In Reciprocal Transformation , an attribute x is replaced by its inverse i.e., 1/x . This transformation can be applied only to attributes having non-zero values. It is also called inverse transformation .

Square Transformation

In Square Transformation , attribute x is replaced by its square (x 2 ). It can be applied to any feature having numeric values.

Square Root Transformation

In this transformation, x is replaced by its square root. It can be applied to features having only positive values. This transformation has a moderate effect on input distribution.

Box-Cox Transformation

In Box-Cox Transformation , values in attribute x are replaced based on the formula as mentioned below :

This transformation will also convert non-normal data to a near-normal distribution.

How does Data Transformation Works?

The entire process of Data Transformation is called ETL (Extract, Transform, and Load) . ETL process can be defined as a six-step process as shown below :

Data Discovery: In this stage, Data Scientists and Analysts identify data sources that are relevant for required for further analysis. They also review its format and schema.

Data Mapping: During this phase, Data Scientists and Analysts determine how individual attributes across data sources are mapped, modified, and aggregated.

Data Extraction : ted from its primary source in this step. It could be in this step SQL database or data from the Internet using Web Scraping methods .

Code Generation and Execution: In this step, Data Scientists and Analysts prepare the code scripts for transformation and execute them.

Review: After the execution of code in the previous step, in this step, the output is reviewed to validate whether the transformation was accurate or not.

Sending: Once transformed data is reviewed and validated, it is sent for storage to the destination source, such as a database, data warehouse, data lake, etc.

Data Transformation Tools

There are various ETL tools available that can be used to implement Data Transformation. ETL tools automate the entire transformation process and can enable efficient monitoring and management of Data Transformation.

ETL tools can be on-premise or cloud-based. On-premise ETL tools are hosted on company servers, and cloud-based ETL tools are hosted in the cloud. Cloud-based ETL tools are easy to scale and use only whenever required to save up extra costs.

Differentiate Between Scaling, Normalization, and Log Transformations

Data Normalization , Data Scaling (Standardization) , and Log Transformation are the most popular transformation techniques used in Data Science. Let’s review how to differentiate between them and which one to choose for your analysis.

• Data Scaling transforms features in such a way that ensures it has zero mean and unit standard deviation. It is the most used transformation technique and typically works for all distributions to transform them into a normal distribution.
• Data Normalization technique transforms features into a fixed range to ensure all values lie between the given min and max values. It doesn’t work well with features that contain a lot of outliers.
• Log Transformation technique is used to transform features with a heavily skewed distribution into a normal distribution.

Ready to dive into the world of data science? Join our free Data Science course and unlock the secrets to harnessing the power of data.

• Data Transformation is a process to transform raw data into a more suitable format that supports efficient data mining and model building. A Data Transformation process is also called an ETL process and this process consists of six steps - data discovery, mapping, extraction, scripting, review, and storing.
• There are many techniques available for Data Transformation. The most common ones include Data Scaling, Data Normalization, Log Transformation, etc.

Related Topics

• What is Data Science?
• How to Become a Data Scientist?

Unlock Data’s Potential: A Step-by-Step Data Transformation Guide

by Sole Galli | Feb 14, 2023 | Data Preprocessing , Feature Engineering , Machine Learning

Data is the lifeblood of any organization, but raw data on its own is not enough. To unlock its full potential, you need to transform it into valuable insights that can drive decision-making, improve operations, and increase revenue. That’s where data transformation comes in.

Data transformation lets you take your data from chaos to clarity and achieve your goals with the right tools and methods. This article will provide a comprehensive guide to maximizing your data’s value through effective transformation processes and data management.

Here, we’ll introduce you to the different types of data transformation, explain why it’s important, and outline the steps you need to take. We will also share frequently-used methods and tools for data transformation and the common challenges you may encounter.

What is Data Transformation?

Data transformation converts raw data into a form used for analysis and decision-making. The goal of data transformation is to improve the quality, structure, format, and visualization of the data so that it can be more easily understood and utilized by end-users, applications, APIs, and business intelligence tools.

The data transformation process can involve various methods and techniques, such as normalization, aggregation, smoothing, and data mapping, to clean, organize, and prepare the data for further use.

Raw data usually comes from multiple sources in different formats (e.g., CSV files, E xcel sheets, XML format). To make it usable for business intelligence or machine learning tasks, you need to work on the enrichment of data quality and compatibility.

Data transformation usually occurs before any statistical analysis or machine learning algorithms are applied to the data set. Data transformation helps streamline business processes by helping you quickly identify trends in large datasets, automate repetitive tasks, and gain insights about customer preferences and behaviors.

The insights from data transformation can help data analysts make better decisions, optimize their operations, and achieve their business goals.

What Are The Common Types of Data Transformation?

Data scientists use different types of data transformation to make raw data usable. The four main categories are:

Constructive Transformation

Constructive transformation involves adding information to the dataset. This can include replacing missing values, correcting errors, and filling in gaps. For example, you can use constructive transformation to add a customer’s contact information or address if it is missing from your original dataset.

Destructive Transformation

Destructive transformation deletes unnecessary data from the dataset. This helps you focus on the most relevant information and reduce noise in the dataset. Common examples of destructive transformations include removing duplicates, deleting irrelevant columns, and removing any outliers.

Aesthetic Transformation

Aesthetic transformation improves a dataset’s overall look and feels by enhancing the data formats for better visualization. This could include sorting data into columns, adding colors, or changing fonts.

For example, formatting the data for improved data analytics, such as converting date and time data into a standardized format.

Structural Transformation

Finally, data engineers use structural transformation to alter your data’s structure and source systems to make analysis easier. It can also combine multiple data sources into one large source so you can gain deeper insights from it.

Common examples of structural transformations are normalizing data across data pipelines, converting strings to numbers for quantitative analysis, and using pivot tables for better organization.

What Are The Steps in Data Transformation Process?

Data science professionals have developed a series of standardized steps for data transformation, which we will explain in detail below:

Step 1: Data Discovery

The first step of the data transformation process is to identify and collect the relevant datasets through data profiling. This includes understanding your source systems, clarifying what you need from them, and understanding the data structure. Your sources may include data from databases, spreadsheets, or other platforms.

Step 2: Data Mapping

Now, you need to map out how your datasets will interact with each other to create an efficient workflow. Here, analysts decide how individual fields are matched, filtered, joined, and aggregated to create a suitable format for analysis.

Step 3: Extraction

During the extraction phases, analysts move data from its source system into the target system, which may include structured (database) or unstructured (event streaming) sources depending on the needs of the project/application at hand.

Step 4: Generating and Executing Codes

You need to generate codes for each step in your mapping process at this stage using a programming language such as Python or SQL. This code should contain instructions on the transformation process and which datasets are involved at each step.

You can then program the codes in a set schedule to get meaningful data in real-time.

Step 5: Reviewing Outputs

Data analysts must also review the transformed data for accuracy and completeness by comparing it against pre-defined quality metrics or other standards. They should also address discrepancies between the expected and generated output with further coding or manual adjustments.

Step 6: Forwarding to Target Destination

The final step is to forward the transformed data to its target destination. This could involve sending it to a database, file system, cloud data warehouse like Snowflake, or another application for further analysis and usage.

Note that data transformation is a cyclical and ongoing process that requires constant monitoring and adjustment.

Frequently-Used Data Transformation Methods

Now, coming to the more technical side of data transformation, let’s look at the different methods and processes you can use to make your data more meaningful. Here is a list of commonly used approaches:

Aggregation

Aggregation involves combining data from multiple sources into a single dataset to create a unified view of data from disparate systems. For example, you may aggregate sales data by product or region to get an overview of your sales performance.

Smoothing removes noise or meaningless data from a dataset to make the data more manageable and easy to analyze. Data analysts often use this to reduce volatility in time-series data and make trends more visible by making small changes.

Generalization

Generalization involves reducing the level of detail in a dataset. For example, you may generalize customer data by grouping customers into segments based on similar characteristics.

This method involves replacing detailed data points with more general ones. For example, you can generalize a dataset containing personal details by replacing names and addresses with codes.

Discretization

Discretization divides continuous data  into a finite number of intervals or categories to make analyzing and interpreting data easier, especially when working with large datasets.

Discretization helps handle continuous attributes in datasets such as age, income, etc. For instance, you can discretize a continuous attribute such as age into three categories- young (18-30 years), middle-aged (31-50 years), and old (> 50 years).

Attribute Construction

Data scientists create new attributes or variables based on existing data through the attribute construction process. This method involves  feature engineering , where you can create unique attributes from existing ones by combining multiple fields.

It helps you identify patterns or relationships between different data points that would not be obvious in the raw data. For example, you could construct an attribute for “total sales” by summing up the values of individual transactions over a certain period.

Normalization

You can scale your data through normalization to fit within a specified range and ensure data consistency across different datasets. Normalization also makes comparing other variables easier and helps reduce data redundancy.

Manipulation

Data manipulation, also sometimes referred to as data wrangling, involves changing or formatting data to make it more suitable for further analysis or presentation. This could include converting text into numbers, sorting values, or removing unnecessary information from datasets before you can use them for further processing.

For more details about data transformations including Python code, check out our course Feature Engineering for Machine Learning .

Variable Transformation for Data Normalizing and Smoothing

To effectively deal with data smoothing and normalizing processes, “variable transformation” is a valuable tool. By applying several mathematical functions known as  variance stabilizing transformations , you can transform variables and reduce their skewness in the value spread across them.

Common functions include:

• Logarithms,
• Reciprocals,
• Square roots,
• Yeo-Johnson transformation, and
• Box-Cox transformation.

You can use these mathematical functions to reveal linear or additive relationships between target variables and predictors that may have been previously obscured.

Types of Data Transformation Tools

Data transformation tools are essential components of the modern data-driven world. By enabling users to transform and migrate data from disparate sources into a single, centralized repository, companies can quickly derive insights from their collected information.

Many data transformation tools are available today, ranging from essential scripting solutions such as SQL or Python to more advanced low/no-code platforms like Zuar’s Mitto that provide an interactive GUI for collecting and loading data into a warehouse.

Scripting Tools

Scripting tools are designed for technical users and developers familiar with SQL or Python programming. These tools allow you to write scripts, automate the data transformation process, and achieve scalability.

A typical example of such a tool is dbt, which combines the functionalities of SQL and Python to create tables and transformations. The Python Mitto SDK is another helpful tool for scripting and automation, letting you interact with multiple business functions and schedules.

Low/No-Code Tools

On the other hand, these tools offer user-friendly dashboards and are intended for non-technical users who don’t have programming skills. They let you collect data from various sources and load it into a data warehouse using an interactive graphical user interface.

Over the past decade, many low-code solutions and Extract-Transform-Load (ETL tools) have emerged, such as Zuar’s Mitto. Mitto provides data integration like ETL/ELT capabilities and helps with the ETL process and management of data throughout its journey. With options for hosting either on-premise or in the cloud implementation, Mitto has code and no-code options to accommodate different skill levels and preferences.

Why Should You Do Data Transformation?

Data transformation is a crucial part of data analysis and management. It allows you to clean, organize, and standardize your datasets to use in analytics or other business operations effectively. The process offers several benefits, such as:

• Improved usability – Computer and human analysts find it easier to work on transformed and organized data.
• Enrichment of data quality – Data transformation eliminates potential quality issues, preventing misinterpretations of the data.
• More efficient query handling – Standardizing the data and storing it properly help with improved data warehousing and will optimize query speed, decreasing friction to analysis.
• Streamlined data management – Metadata and lineage tracking implemented through transformation make it easier for teams to manage data gathered from different sources.

While data transformation methods have numerous benefits, it’s essential to understand that a few potential drawbacks exist.

Challenges with Data Transformation

The process of transforming raw data into valuable insights and information can present many challenges for organizations, as follows:

• Cost: One of the biggest challenges is the high pricing of data transformation tools and the need for professional expertise.
• Resource demand: Another challenge organizations face is the demanding compute resources required for the data transformation process. The intensity of some on-premises transformation processes can take a toll on other business functions.
• Skill shortage: The shortage of skilled data professionals is another major challenge organizations face regarding data transformation. Data professionals are in high demand and can be difficult to recruit and retain, which can pose a challenge.
• Priority handling: Finally, aligning data transformation activities with the business’s data-related priorities and requirements can be challenging. It’s vital for organizations to properly prioritize their data transformation goals and ensure that their transformation activities align with their overall data strategy. Failure to do so can result in a confusing and ineffective transformation process.

Wrap-Up: Master Data Transformation Process

Whether it’s for better data organization, improved data quality, faster queries, or simplified data management, the benefits of data transformation are clear. However, with any complex process, there are also challenges to be aware of, including high costs, resource constraints, and the need for skilled data professionals.

Despite these challenges, organizations can overcome them by carefully planning their transformation processes. The  Python Feature Engineering Cookbook  and  Feature Engineering for Machine Learning course  are two excellent resources to help you get started with mastering data transformation.

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What is Data Transformation?

Data transformation is a critical step in data analysis process, encompassing the conversion, cleaning, and organizing of data into accessible formats. This process is critical for businesses looking to utilize their data to generate timely business insights, as it ensures that the information is accessible, consistent, secure, and finally recognized by the intended business users.

In this article, we’ll understand the introductory knowledge of data transformation, its many operations, methodologies, and tools, as well as its benefits, drawbacks, and best practices.

Table of Content

Understanding Data Transformation

Importance of data transformation, key data transformation operations for effective analysis, data transformation techniques and tools, advantages and limitations of data transformation, best practices for data transformation, applications of data transformation.

The process of data transformation, involves converting, cleansing, and structuring data into a usable format which is used to analyzed to support decision-making processes. It includes modifying the format, organization, or values of data to prepare it for consumption by an application or for analysis. This crucial process is undertaken by organizations seeking to leverage their data to provide timely business insights, ensuring that the information is accessible, consistent, safe, and eventually acknowledged by the targeted business users.

The transformations can be divided into two categories: Simple transformations and Complex Data Transformations.

• Simple Data Transformations include straightforward procedures including data cleansing, standardization, aggregation, and filtering. These transformations are often carried out utilizing simple data manipulation methods and are frequently used to prepare data for analysis or reporting.
• Complex Data Transformations include more advanced processes such data integration, migration, replication, and enrichment. These transformations often need complex data manipulation methods like as data modeling, mapping, and validation, and are commonly used to prepare data for advanced analytics, machine learning, or data warehousing applications.

Data transformation is important because it improves data quality, compatibility, and utility. The procedure is critical for companies and organizations that depend on data to make informed decisions because it assures the data’s accuracy, reliability, and accessibility across many systems and applications.

• Improved Data Quality: Data transformation eliminates mistakes, inserts in missing information, and standardizes formats, resulting in higher-quality, more dependable, and accurate data.
• Enhanced Compatibility : By converting data into a suitable format, companies may avoid possible compatibility difficulties when integrating data from many sources or systems.
• Simplified Data Management: Data transformation is the process of evaluating and modifying data to maximize storage and discoverability, making it simpler to manage and maintain.
• Broader Application: Transformed data is more useable and applicable in a larger variety of scenarios, allowing enterprises to get the most out of their data.
• Faster Queries: By standardizing data and appropriately storing it in a warehouse, query performance and BI tools may be enhanced, resulting in less friction during analysis.

Various methods of data transformation exist, depending on the complexity and nature of the process:

• Normalization : Modifying data scales, such as scaling values from 0 to 1, to enable comparisons.
• Standardization : Transforming data to have a unit variance and zero mean, which is frequently required before using machine learning methods.
• Encoding: Transforming categorical data into numerical representations using label or one-hot encoding, for example.
• Discretization: Converting continuous data into discrete bins, which in some circumstances can facilitate analysis and enhance model performance.
• Attribute Generation: Creating new variables from existing data, such as deriving an ‘age’ variable from a date of birth.
• Revising : Ensuring that the data supports its intended usage by deleting duplicates, standardizing the data collection, and purifying it.
• Manipulation: Creating new values from existing ones or changing the state of data through computing.
• Separating: Splitting down data values into component for filtering on certain values.
• Combining/Integrating: Bringing together data from several tables and sources to provide a comprehensive picture of an organization.
• Binning or Discretization: Continuous data can be grouped into discrete categories, which is helpful for managing noisy data.
• Smoothing: Methods like moving averages can be applied to reduce noise in time series or create smoothed data.
• Logarithmic or Exponential Transformation : Data distribution can be altered through logarithmic or exponential functions for specialized analyses.
• Text Preprocessing : Text data can be prepared for NLP (Natural language processing) tasks by tokenizing, stemming , or lemmatizing.

There are several ways to alter data, including:

• Programmatic Transformation: automating the transformation operations via the use of scripts or computer languages such as Python, R, or SQL.
• ETL Tools: Tools for extracting, transforming, and loading data (ETL) are made to address complicated data transformation requirements in large-scale settings. After transforming the data to meet operational requirements, they extract it from several sources and load it into a destination like a database or data warehouse.
• Normalization/Standardization: Scikit-learn in Python provides functions for normalization and standardization such as MinMaxScaler and StandardScaler.
• Encoding Categorical variables:  Pandas librayr in python provides   get_dummies function employed for one-hot encoding . For label encoding LabelEncoder is provided by Scikit-learn.
• Imputation:  Missing values in the dataset are filled using statistical methods like  fillna method in Pandas Library. Additionally, missing data can be imputed using mean, median, or mode using scikit-learn’s SimpleImputer .
• Feature Engineering: To improve model performance, new features are developed by combining old ones. Pandas, a Python library, is often used to execute feature engineering tasks. Functions such as apply, map, and transform are used to generate new features.
• Aggregation and grouping:    Pandas groupby function is used to group data and execute aggregation operations such as sum, mean, and count.
• Text preprocessing: Textual data is preprocessed by tokenizing, stemming, and eliminating stop words using NLTK and SpaCy Python libraries.
• Dimensional Reduction: The technique involves reducing the amount of characteristics while retaining vital information. Scikit-learn in Python provides techniques such as PCA for Principal Component Analysis and TruncatedSVD for Dimensionality Reduction.

Advantages of Data Transformation

• Enhanced Data Quality: Data transformation aids in the organisation and cleaning of data, improving its quality.
• Compatibility: It guarantees data consistency between many platforms and systems, which is necessary for integrated business environments.
• Improved Analysis: Analytical results that are more accurate and perceptive are frequently the outcome of transformed data.

Limitations of Data Transformation

• Complexity: When working with big or varied datasets, the procedure might be laborious and complicated.
• Cost: The resources and tools needed for efficient data transformation might be expensive.
• Risk of Data Loss: Inadequate transformations may cause important data to be lost or distorted.

A few pragmatic aspects need to be kept in mind when transforming data:

• Knowing the Data: It’s critical to have a thorough grasp of the data, including its type, source, and intended purpose.
• Selecting the Appropriate Tools: The right tools, from basic Python scripting to more complicated ETL tools, should be chosen based on the quantity and complexity of the dataset.
• Observation and Verification: To guarantee that the data transformation processes produce the desired outputs without causing data loss or corruption, ongoing validation and monitoring are essential.

Applications for data transformation are found in a number of industries:

• Business intelligence (BI) is the process of transforming data for use in real-time reporting and decision-making using BI technologies.
• Healthcare: Ensuring interoperability across various healthcare systems by standardization of medical records.
• Financial Services: Compiling and de-identifying financial information for reporting and compliance needs.
• Retail: Improving customer experience through data transformation into an analytics-ready format and customer behavior analysis.
• Customer Relationship Management (CRM): By converting customer data, firms may obtain insights into consumer behavior, tailor marketing strategies, and increase customer satisfaction.

Data transformation is an important phase allowing firms to get useful insights from their data. Data transformation improves data quality, interoperability, and usefulness, allowing for more informed decision-making and supporting a broad variety of applications across sectors. While there are significant problems and restrictions, the advantages of data translation far exceed the expenses. Organizations may achieve a competitive advantage in their specific industries by comprehending the data, choosing the relevant tools, and monitoring and confirming the transformation process.

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Data transformation: The foundation of analytics work

Data transformation is the process of taking raw source data and using SQL and Python to clean, join, aggregate, and implement business logic to create important datasets. These end datasets are often exposed in a business intelligence (BI) tool and form the backbone of data-driven business decisions. Data transformation is a key component of the ETL / ELT process where the “T” represents the data transformation stage, and is typically performed by the analytics engineer on the team or, depending on the organizational structure and needs, data analysts or data engineers.

Without data transformation, analysts would be writing ad hoc queries against raw data sources, data engineers will be bogged down in maintaining deeply technical pipelines, and business users will not be able to make data-informed decisions in a scalable way.

As a result, data transformation is at the heart of a business: good transformation creates clear, concise datasets that don’t have to be questioned when used, empowers data analysts to take part in the analytics workflow, and presents a series of challenges that keeps analytics work interesting 😉

Benefits of data transformation #

Why is data transformation the foundation for modern data analytics? Because it’s the baseline for increasing the data quality of your business and creating meaningful data for your end users.

Increases data quality

Data transformation can increase data quality through the process of standardization, testing, and automation. During the transformation process, raw data is cleaned, casted, converted, joined, and aggregated using SQL and Python to create end datasets that are consumed by business users. In an ideal world, these transformations are version-controlled and peer-reviewed. This transformation process should also follow automated testing practices, ultimately creating tables data analysts and end business users can trust.

By transforming your data with tooling that supports standardization, version control, integrated documentation, modularity, and testing, you leave little room for error. Data analysts don’t need to remember which dataset is in which timezone or currency; they know the data is high-quality because of the standardization that has taken place.

In addition to standardizing raw data sources, metrics can be properly created during the transformation process. dbt supports the creation of metrics and exposing them via the Semantic Layer , ultimately allowing you to create and apply the same metric calculation across different models, datasets, and BI tools, ensuring consistency across your stack. As you develop consistent metric definitions, your data quality increases, trust in your data work increases, and the ROI of a data team becomes much more apparent.

Creates reusable, complex datasets

Data transformation allows you to automate various data cleaning and metric calculations. This ensures consistent, accurate, and meaningful datasets are being generated in the data warehouse each day, or on whatever time cadence your business chooses. By automating certain data models, data analysts do not need to repeat the same calculations over and over again within the BI layer. These data sets can be referenced directly within a report or dashboard instead, speeding up compute time.

Data transformation also activates the reverse ETL process . Transformation allows analytics engineers to join different datasets into one data model, providing all the needed data in one dataset. Because datasets are being automated using data transformation, this data can be ingested into different reverse ETL tools, giving stakeholders the data they need, where and when they need it.

Challenges of data transformation #

Data transformation is fun, but tough work for analytics practitioners. The difficulty often varies given the complexity and volume of your data, the number of sources you’re pulling from, and the needs of your stakeholders. Some of the biggest challenges you’ll face during the data transformation process are: creating consistency, standardizing core metrics, and defining your data modeling conventions.

Consistency across multiple datasets

During the transformation process, it can be challenging to ensure your datasets are being built with standardized naming conventions, following SQL best practices, and conforming to consistent testing standards. You may often find yourself checking if timezones are the same across tables, whether primary keys are named in a consistent format, and if there’s duplicative work across your data transformations. 

How you determine what consistency and standardization look like in your data transformation process is unique to your team and organization. However, we recommend using a tool, such as dbt, that encourages data transformation DRYness and modularity, code-based and automatic tests for key columns, and explorable documentation to help you keep consistent and governable data pipelines. Here are some other dimensions you should keep in mind when trying to create consistency in your data:

• What timezone are your dates and timestamps in?
• Are similar values the same data type?
• Are all numeric values rounded to the same number of decimal points?
• Are your column names named using the same format?
• Are all primary keys being regularly tested for uniqueness and non-nullness?

These are all different factors to consider when creating consistent datasets. Doing so in the transformation stages will ensure analysts are creating accurate dashboards and reports for stakeholders, and analytics practitioners can more easily understand the requirements to contribute to future transformations.

Defining data modeling conventions

Defining data modeling conventions is a must when utilizing data transformation within your business. One of the reasons data transformation is so powerful is because of its potential to create consistent, standardized data. However, if you have multiple analytics engineers or data analysts working on your data models, this can prove difficult. In order to create high-quality, valuable datasets your data team must decide on style conventions to follow before the transformation process begins.

If proper style guidelines are not in place, you may end up with various datasets all following different standards. The goal is to ensure your data is consistent across all datasets, not just across one engineer’s code. We recommend creating a style guide before jumping into the code. This way you can write all of your standards for timezones, data types, column naming, and code comments ahead of time. This will allow your team to create more consistent, scalable, and readable data transformations, ultimately lowering the barrier for contribution to your analytics work.

Standardization of core KPIs

We’ve been there: it’s the end of the quarter and time to close the books on financial reporting. Your CFO pulled X number for revenue, while your head of accounting pulled Y—and this happens every.single.quarter. 

A lack of consistency in key metrics across your business is one of the largest pain points felt by data teams and organizations.

Core organizational metrics should be version-controlled, defined in code, have identifiable lineage, and be accessible in the tools business users actually use. Metrics should sit within the transformation layer, abstracting out the possibility of business users writing inaccurate queries or conducting incorrect filtering in their BI tools.

When you use modern data transformation techniques and tools, such as dbt, that help you standardize the upstream datasets for these key KPIs and create consistent metrics in a version-controlled setting, you create data that is truly governable and auditable. There is no longer a world where your CFO and head of accounting are pulling different numbers: there is only one world where one singular metric definition is exposed to downstream users . The time, energy, and cost benefit savings that comes from a standardized system like this are almost incalculable.

Data transformation tools #

Just like any other part of the modern data stack, there are different data transformation tools depending on different factors like budget, resources, organization structure, and specific use cases. Below are some considerations to keep in mind when looking for a data transformation tool.

Enable engineering best practices

One of the greatest developments in recent years in the analytics space has been the emphasis on bringing software engineering best practices to analytics work. But what does that really mean?

This means that data transformation tools should conform to the practices that allow software engineers to ship faster and more reliable code—practices such as version control, automatic testing, robust documentation, and collaborative working spaces. You should consider whether or not a data transformation tool offers version control, or connects directly with your git provider, so that you can keep track of transformation code changes over time. Your data transformation tool should also support transformations-as-code, allowing anyone who knows SQL can partake in the data transformation process.

Build vs buy

Like all internal tooling, there will come a time and place when your team needs to determine whether to build or buy the software and tooling your team needs to succeed. When considering building your own tool, it’s vital to look at your budget and available resources:

• Is it cheaper to pay for an externally managed tool or hire data engineers to do so in-house? 
• Do you have enough engineers to dedicate the time to building this tool? 
• What do the maintenance costs and times look like for a home-grown tool? 
• How easily can you hire for skills required to build and maintain your tool?
• What is the lift required by non-technical users to contribute to your analytics pipelines and work?

Factors such as company size, technical ability, and available resources and staffing will all impact this decision, but if you do come to the conclusion that an external tool will be appropriate for your team, it’s important to break down the difference in open source and SaaS offerings.

Open source vs SaaS

If you decide to use an externally created data transformation tool, you’ll need to decide whether you want to use an open source tool or SaaS offering. For highly technical teams, open source can be budget-friendly, yet will require more maintenance and skilled technical team members. SaaS tools have dedicated infrastructures, resources, and support members to help you set up the tool, integrate it into your already-existing stack, and scale your analytics efficiently. Whether you choose open source or SaaS will again depend on your specific budget and resources available to you:

• Do you have the time to integrate and maintain an open source offering? 
• What does your budget look like to work with a SaaS provider? 
• Do you want to depend on someone else to debug errors in your system?
• What is the technical savviness of your team and end business users?

dbt offers two primary options for data transformation: dbt Core, an open source Python library to help you develop your transformations-as-code using SQL and the command line. dbt Cloud, the SaaS offering of dbt Core, includes an integrated development environment (IDE), orchestrator, hosted documentation site, CI/CD capabilities, and more for your transformations defined in dbt. Learn more about the three flexible dbt Cloud pricing options here .

Technical ramp period

Last but definitely not least, you must weigh the technical learning and adoption curves that come with choosing a data transformation tool. You need to ask yourself if those on your data team have the technical expertise to use whatever tool you choose to implement. Can they code in the language the tool uses? If not, how long will it take them to learn? If it’s an open source tool you decide on you may need to consider whether or not your data team is familiar with hosting that tool on their own infrastructure.

It’s also important to note the lift required by your end business users: will they have difficulty understanding how your data pipelines work? Is transformation documentation accessible, understandable, and easily explorable by business users? For folks who likely only know baseline SQL, what is the barrier to contributing?? After all, the countless hours of time and energy spent by data practitioners are to help empower their business users to make the most informed decisions they can using the data and infrastructure they maintain.

The backstory: how modern data transformation came to be #

Let’s back up a bit and ask: why are we even writing this page? Why has the space of data transformation evolved so much in the past 5 years? To answer this question, let’s take a look back to 2016, when dbt was born.

Raw source data was flowing into MPP databases with (relative) ease. At that time, teams were visualizing it in reporting tools like Looker, Tableau, and many others. But the data transformation layer in between was being duct taped together; data teams were writing complex stored procedures with often confusing dependencies. Or sometimes even manually refreshing queries to calculate metrics for visualizations.

Refreshing transformed metrics often required human intervention: when a query failed, a human would re-run each individual query from that point forward in the dependency graph —inferring those dependencies by intuition or experience.

There wasn’t much of a concept of an automatically-generated dependency graph or using version control to define and store transformations. And it turned out that many teams were running into this same challenge: plentiful raw data, a useful tool to visualize it, but nothing in between to get datasets ready for scalable use.

dbt emerged to empower data teams to solve this problem. Read more here how it works and how to get started with your own modern data transformations.

Conclusion #

Data transformation is a fundamental part of the ETL/ELT process within the modern data stack. It allows you to take your raw source data and find meaning in it for your end business users; this transformation often takes the form of modular data modeling techniques that encourage standardization, governance, and testing. When you utilize modern data transformation tools and practices, you produce higher-quality data and reusable datasets that will help propel your team and business forward. While there are very real challenges, the benefits of following modern data transformation practices far outweigh the hurdles you will jump.

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Data Transformation

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Analyzing information requires structured and accessible data for best results. Data transformation enables organizations to alter the structure and format of raw data as needed. Data transformation is the process of changing the format, structure, or values of data. For data analytics projects, data may be transformed at two stages of the data pipeline. Organizations that use on-premises data warehouses generally use an ETL (extract, transform, load) process, in which data transformation is the middle step. Today, most organizations use cloud-based data warehouses, which can scale compute and storage resources with latency measured in seconds or minutes. 

Data transformation may be constructive, destructive, aesthetic or structural. An enterprise can choose among a variety of ETL tools that automate the process of data transformation. Data analysts, data engineers, and data scientists also transform data using scripting languages such as Python or domain-specific languages like SQL.

Transformed data may be easier for both humans and computers to use. Properly formatted and validated data improves data quality and protects applications from potential landmines such as null values, unexpected duplicates, incorrect indexing, and incompatible formats. Data transformation facilitates compatibility between applications, systems, and types of data. Data used for multiple purposes may need to be transformed in different ways.

Data transformation can increase the efficiency of analytic and business processes and enable better data-driven decision-making. The first phase of data transformations should include things like data type conversion and flattening of hierarchical data. These operations shape data to increase compatibility with analytics systems. Data analysts and data scientists can implement further transformations additively as necessary as individual layers of processing. 

Before your enterprise can run analytics, and even before you transform the data, you must replicate it to a data warehouse architected for analytics. Most organizations today choose a cloud data warehouse, allowing them to take full advantage of ELT.

This template can be used by data scientists and analysts in their work. For example, you might think of a roadmap for transforming your data into new storage formats that can help you improve decision-making through processing speed. You can also use the slides in this template to prepare information about increasing data protection and improving the reliability of information storage.

This template can be used by university teachers when preparing their courses on the storage and processing of large amounts of information. Also, this template will be useful for heads of startups when preparing to meet with potential investors. You can use the slides in this template to explain information storage and processing techniques when building business processes in your company.

Data Transformation Template consists of four professional and stylish slides. You can independently change the arrangement of blocks and infographics, as well as customize the color scheme according to your corporate requirements. The Data Transformation Template is a must-have for analysts and data scientists. This template’s slides will organically complement and update your old presentations.

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Data Transformation: Benefits, Types, and Processes

Lukas Racickas

Data transformation involves converting data from one format into another for further processing, analysis, or integration. The data transformation process is an integral component of data management and data integration. Likewise, companies can improve their data-driven decision-making by streamlining their data management and integration processes through data transformation. 

However, as more and more companies adopt cloud-based data storage ( IDC reports that today 67% of enterprise infrastructure is cloud-based), the data transformation process must follow suit. Consequently, many companies are searching for public web data integration processes and data transformation tools that help improve the data quality, readability, and organization company-wide.

In this article, I will explore the data transformation process, how it contributes to the broader processes of data integration, as well as new data transformation technologies. 

Benefits of data transformation

From a general perspective, data transformation helps businesses take raw data (structured or unstructured) and transform it for further processing, including analysis, integration, and visualization. All teams within a company’s structure benefit from data transformation, as low-quality unmanaged data can negatively impact all facets of business operations. Some additional benefits of data transformation include: 

• Improved data organization and management  
• Increased computer and end-user accessibility
• Enhanced data quality and reduced errors
• Greater application compatibility and faster data processing 

Data integration

Before examining the various ways to transform data, it is important to take a step back and look at the data integration process. Data integration processes multiple types of source data into integrated data, during which the data undergoes cleaning, transformation, analysis, loading, etc. With that, we can see that data transformation is simply a subset of data integration. 

Data integration as a whole involves extraction, transformation, cleaning, and loading. Over time, data scientists have combined and rearranged these steps, consequently creating four data integration processes: batch, ETL, ELT, and real-time integration. 

Batch integration

Another common method is batch data integration, which involves moving batches of stored data through further transformation and loading processes. This method is mainly used for internal databases, large amounts of data, and data that is not time-sensitive. 

ETL integration

Similar to ELT, ETL data processing involves data integration through extraction, transformation, and loading. ETL integration is the most common form of data integration and utilizes batch integration techniques. 

ELT integration

ELT data processing involves data integration through extraction, loading, and transformation. Similar to real-time integration, ELT applies open-source tools and cloud technology, making this method best for organizations that need to transform massive amounts of data at a relatively quick pace. 

Real-time integration

One of the more recent data integration methods, real-time integration, processes and transforms data upon collection and extraction. This method utilizes CDC (Change Data Capture) techniques, among others, and is helpful for data processing that requires near-instant use. 

These same concepts utilized in data integration have also been applied to the individual steps within the larger integration process, such as data transformation. More specifically, both batch data processing and cloud technology, utilized in real-time integration, have been crucial in developing successful data transformation processes and data transformation tools. Now, let’s take a closer look at the types of data transformation processes. 

First party data (data you collect yourself about your company and your customers) is rapidly growing in value. Your ability to transform and use that data to drive decisions and strategies will increasingly become the source of competitive advantage. - Rich Edwards, CEO of Mindspan Systems

Types of data transformation

Batch data transformation.

Batch data transformation, also known as bulk data transformation, involves transforming data in groups over a period of time. Traditional batch data transformation involves manual execution with scripted languages such as SQL and Python and is now seen as somewhat outdated. 

More specifically, batch transformation involves ETL data integration, in which the data is stored in one location and then transformed and moved in smaller batches over time. It is important to note the significance of batch data transformation on many data integration processes, such as web application integration, data warehousing, and data virtualization. When applied to other data integration processes, the concepts and logistics within batch data transformation can improve the overall integration process. 

Interactive data transformation

As many companies turn to cloud-based systems, IBM even reports that 81% of companies use multiple cloud-based systems , end-users of said data are also looking for more versatile methods to transform data. Interactive data transformation, also referred to as real-time data transformation uses similar concepts seen in real-time integration and ELT processing. 

Interactive data transformation is an expansion of batch transformation. However, the steps are not necessarily linear. Gaining traction for its accessible end-user visual interface, interactive data transformation takes previously generated and inspected code to identify outliers, patterns, and errors within the data. It then sends this information to a graphical user interface for human end-users to quickly visualize trends, patterns, and more, within the data. 

Data transformation languages

In addition to the various types of data transformation, developers can also utilize a variety of transformation languages to transform formal language text into a more useful and readable output text. There are four main types of data transformation languages: macro languages, model transformation languages, low-level languages, and XML transformation languages. 

The most commonly used codes in data transformation include ATL, AWK, identity transform, QVT, TXL, XQuery, and XSLT. Ultimately, before deciding what transformation method and language to use, data scientists must consider the source of the data, the type of data being transformed, and the project’s objective.

The data transformation process

Now that we’ve covered the bigger picture of how data transformation fits into the larger picture of data integration, we can examine the more granular steps in data transformation itself. Firstly, it is important to note that while it's possible to transform data manually, today, companies rely on data transformation tools to partially or fully transform their data. Either way, manual and automated data transformation involves the same steps detailed below. 

1. Data discovery and parsing

The first step in the data transformation process involves data discovery and data parsing . Data discovery and data parsing are processes that involve collecting data, consolidating data, and reorganizing data for specific market insights and business intelligence. At Coresignal, we can offer you parsed, ready-to-use data.

2. Data mapping and translation

Once you have profiled your data and decided how you want to transform your data, you can perform data mapping and translation. Data mapping and translation refer to the process of mapping, aggregating, and filtering said data so it can be further processed. For example, in batch transformation, this step would help filter and sort the data in batches so executable code can run smoothly.

3. Programming and code creation

The data programming involves code generation, in which developers will work with executable coding languages, such as SQL, Python, R, or other executable instructions. During this stage, developers are working closely with transformation technologies, also known as code generators. Code generators provide developers with a visual design atmosphere and can run on multiple platforms, making them a favorite among developers. 

4. Transforming the data

Now that the code is developed, it can be run against your data. Also known as code execution, this step is the last stage the data passes through before reaching human end-users.

5. Reviewing the data 

Once the code executes the data, it is now ready for review. Similar to a quality assurance check, the purpose of this step is to make sure the data has been transformed properly. It is important to note that this step is iterative, in that end-users of this data are responsible for reporting any errors they found in transformed data to the developers, so edits to the code can be made. 

Data extraction and transformation have an effect on other business activities. When data is transformed into a more readable format, data analysis can be completed more quickly and accurately than before. Not only does this have an effect on employee morale, but it also has an impact on company decision-making. - Brian Stewart, CTO of ProsperoWeb

ETL vs. ELT

The recent advancements in big data have required businesses to look elsewhere when storing, processing, and analyzing their data. Moreso, the increasing variety in data sources has also contributed to the strain being placed on data warehouses. Particularly, while companies acquire powerful raw data from data types such as firmographic data, employee data, and social media data, these same data types typically export very large file sizes. Consequently, companies have been searching for alternative methods. 

This search has greatly impacted data integration processes, specifically data transformation. That is, companies have been transitioning from traditional data integration processes, such as ETL methods, to cloud-based integration processes, such as ELT and real-time integration. 

In the past, many companies have relied on local servers for data storage, making ETL integration the preferred method. However, due to the significant increase in digital communication and business operations in 2020, global data creation is now modeled at a CAGR of 23% , according to Businesswire. Subsequently, the upward trend in global data creation has put a strain on local servers and data storage, and many businesses are looking elsewhere for cloud-based solutions. 

What is data transformation in ETL? 

ETL, which stands for extraction, transformation, and loading, is a data integration process that involves extracting data from various external sources, often from third-party data providers , transforming the data into the appropriate structure, and then loading that data into a company’s database. The ETL process is considered the most common integration process compared to ELT, ETM, and EMM transformation processes.

Data transformation within ETL occurs in the transformation step; however, it is closely linked to the extraction and loading stages. Traditionally, data transformation within the ETL method utilizes batch transformation with linear steps, including discovery, mapping, programming, code execution, and data review. 

As businesses collect an increasing volume of data, many are forced to find data storage and processing solutions that can handle massive amounts of data with limited money and resources. Similarly, companies are also looking for data transformation solutions that can meet the current needs and industry standards. Companies are recognizing the future of data transformation and shifting towards utilizing cloud-based technology in processes such as ELT integration and interactive data transformation. 

This article was originally published on Data Science Central .

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Blog > Big Data > 3 Real-World Examples of Data Transformation

3 Real-World Examples of Data Transformation

Data transformation is a buzzword you hear frequently in the age of big data (even though data transformation’s significance is not limited just to big data). And while it’s easy to define data transformation at a high level, understanding what data transformation means in practice can be trickier. If you’ve found yourself pondering what data transformation examples look like, keep reading for some real-world situations in which data needs to be transformed, and what the transformation requires.

What is data transformation?

As the term implies, data transformation means taking data stored in one format and converting it to another.

As a computer end-user, you probably perform basic data transformations on a routine basis. When you convert a Microsoft Word file to a PDF, for example, you are transforming data.

But data transformation plays a more important and sophisticated role in big data analytics. That is because when you are dealing with large volumes of data, different types of data analytics tools and different data storage systems, you are likely to encounter situations where a large amount of data needs to be transformed from one format to another.

So, that’s the high-level definition of data transformation. To illustrate the concept more precisely, let’s take a look at some data transformation examples.

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Character encoding and data transformation

Character encoding problems are a common reason for data transformation. To understand why, you first have to understand the point of character encoding.

Character encoding lets computers represent individual letters (or other characters) based on codes. In other words, every letter in the alphabet (as well as other text symbols, such as commas or periods) can be represented as a code. As long as your computer knows which particular code was used to represent letters in a given set of data, it can decode the data in order to translate it into letters and numbers and print them on your screen.

The problem that often arises out of character encoding is that sometimes, one application encodes characters using one type of encoding scheme, then sends that information to another application that uses a different encoding scheme by default. When the second application tries to open the data, it may not be able to interpret all of the characters.

If you have ever opened a file and found that some of the letters or numbers inside the text are represented as gibberish or seemingly random symbols, there is a decent chance that character encoding inconsistency is the reason.

Today, in order to prevent encoding issues, most computers following the UTF-8 encoding scheme – or a newer scheme that is backwards-compatible with it. But it still happens that an application encodes data in a way that other applications or computers do not expect. In these cases, the data would need to be transformed from one type of character encoding format to another.

CSV to XML transformation

CSV (short for comma-separated values) and XML (or extensible markup language) are two popular ways of storing data. But they work quite differently.

In a CSV file, you use commas to distinguish different data values from each other. In XML, you primarily represent data using tags that define different units of data and their values. Both types of files also make it possible to represent data hierarchies, but they do so in different ways.

Because CSV and XML work so differently, an application that was designed to read and write data in CSV format can’t typically open an XML file, and vice versa. This is where data transformation comes in.

Using a data transformation tool, you can automatically convert data from a CSV file into XML format so that you can open it with the right tools.

Transforming speech to text

A third data transformation example – and one that highlights just how broadly you should think about data transformation – is a situation in which you have human speech that is stored in an audio file, but want to translate that information into a text file.

While you could technically open an audio file as a text file (by, for example, running a command like “cat myfile.wav” in your terminal on Linux or macOS), the audio file would not make much sense when opened this way. It would be a bunch of gibberish.

To make the audio data actually usable to humans who can’t listen to it – or to transform it into a format that allows it to be parsed automatically by an application that reads text – you would want to transform the speech in the audio file into a text file.

You could do this manually by listening to the audio and transcribing the speech in it. Or you could use a speech-to-text tool to automate the process. If you were transforming data on a large scale, you would want to take the automatic approach.

This example might not be one of the first to come to mind when data transformation specialists think of data transformation examples, because it involves more than dealing with data formatting differences. But, it’s an example of data transformation in a broad sense. It’s also a scenario that you may well face if, for example, you record phone calls with customers and want a way of making data from the conversations available for parsing by tools that can only interpret textual information.

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Data Transformation

Sep 15, 2014

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Data Transformation. Objectives: Understand why we often need to transform our data The three commonly used data transformation techniques Additive effects and multiplicative effects Application of data transformation in ANOVA and regression. Why Data Transformation?.

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Presentation Transcript

Data Transformation • Objectives: • Understand why we often need to transform our data • The three commonly used data transformation techniques • Additive effects and multiplicative effects • Application of data transformation in ANOVA and regression.

Why Data Transformation? • The assumptions of most parametric methods: • Homoscedasticity • Normality • Additivity • Linearity • Data transformation is used to make your data conform to the assumptions of the statistical methods • Illustrative examples

Homoscedasticity and Normality The data deviates from both homoscedasticity and normality.

Homoscedasticity and Normality Won’t it be nice if we would make data look this way?

Types of Data Transformation • The logarithmic transformation • The square-root transformation • The arcsine transformation. • Data transformation can be done conveniently in EXCEL. • Alternatives: Ranks and nonparametric methods.

Homoscedasticity • The two groups of data seem to differ greatly in means, but a t-test shows that the means do not differ significantly from each other - a surprising result. • The two groups of data differ greatly in variance, and both deviate significantly from normality. These results invalidate the t-test. • We calculate two ratios: var/mean ratio and Std/mean ratio (i.e., coefficient of variation). • Group1 Group2Var/mean 56.420 416.891C.V. 1.230 1.230 • Log-transformation

Log-Transformed Data NewX = ln(X+1) • The transformation is successful because: • The variance is now similar • Deviation from normality is now nonsignificant • The t-test revealed a highly significant difference in means between the two groups 1.31 2.13

Log-Transformed Data Transform back: NewX = ln(X+1) Compare this mean with the original mean. Which one is more preferable? Calculate the standard error, the degree of freedom, and 95% CL (t0.025,16 = 2.47).

Normal but Heteroscedastic Any transformation that you use is likely to change normality. Fortunately, t-test and ANOVA are quite robust for this kind of data. Of course, you can also use nonparametric tests.

Normal but Heteroscedastic The two variances are significantly different. The t-test, however, detects significant difference in means. You can use nonparametric methods to analyse data for comparison, and you are like to find t-test to be more powerful.

Additivity • What experimental design is this? • Compare the group means. Is there an interaction effect? Additivity means that the difference between levels of one factor is consistent for different levels of another factor.

Multiplicative Effects • Compare the group means. Is there an interaction effect? • Does this data set meet the assumption of additivity? • When the assumption of additivity is not met, we have difficulty in interpreting main effects. • Now calculate the ratio of group means. What did you find?

Multiplicative Effects For Factor A, we see that Level 2 has a mean about 2.88 times as large as that for Level 1. For factor B, Level 2 has a mean about 2.18 times as large as that for Level 1). If you know the value for Level 1 of Factor A, you can obtain the value for Level 2 of Factor A by multiplying the known value by 2.88. Similarly, you can do the same for Factor B. We say that the effect of Factors A and B are multiplicative, not additive.

Log-transformation Original Data 37.262 108.458 2102.351 17878.648 82.403 234.508 12400.091 80241.944 3.084 4.127 1.302 1.268 3.778 4.803 1.235 1.385 Transformed data Mean Variance Now log-transform the data. Compare the means. Is the assumption of additivity met now? 1.31 2.13

Why log-transformation can change the multiplicative effects to additive effects?

Square-Root Transformation • The two groups of data differ much in variance. • Calculate two ratios: var/mean ratio and Std/mean ratio (i.e., coefficient of variation). • Does your calculation suggest log-transformation? When is log-transformation appropriate? • Use square-root transformation when different groups have similar Variance/Mean ratios Notice the means, which do not coincide with the most frequent observations

Square-Root Transformation Square-root transformation: Transform the means back to the original scale and compare these means with the original means: 1.17 2.09 The variance is now almost identical between the two groups

Quiz on Data Transformation The data set is right-skewed for each group. Calculate the variance/mean ratio and C.V. for each group, and decide what transformation you should use. Do the transformation and convert the means back to the original scale.

With Multiple Groups When you have multiple groups, a “Variance vs Mean” or a “Std vs Mean” plot can help you to decide which data transformation to use. The graph on the left shows that the Var/Mean ratio is almost constant. What transformation should you use?

Confidence Limits Before transformation After transformation With the skewness in our data, do confidence limits on the right make more sense? Why?

Arcsine Transformation • Used for proportions • Compare the variances before and after transformation • Do you know how to transform the means and C.L. back to the original scale?

Data Transformation Using SAS Data Mydata; input x; newx=log(x); newx=sqrt(x+3/8); newx=arsin(sqrt(x)); cards; Natural logarithm transfromation Square-root transformation Arcsine transformation

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Press Release Details

Nvidia nim revolutionizes model deployment, now available to transform world’s millions of developers into generative ai developers.

• 150+ Partners Across Every Layer of AI Ecosystem Embedding NIM Inference Microservices to Speed Enterprise AI Application Deployments From Weeks to Minutes
• NVIDIA Developer Program Members Gain Free Access to NIM for Research, Development and Testing

TAIPEI, Taiwan, June 02, 2024 (GLOBE NEWSWIRE) -- COMPUTEX -- NVIDIA today announced that the world’s 28 million developers can now download NVIDIA NIM ™ — inference microservices that provide models as optimized containers — to deploy on clouds, data centers or workstations, giving them the ability to easily build generative AI applications for copilots, chatbots and more, in minutes rather than weeks.

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Enterprises can deploy AI applications in production with NIM through the NVIDIA AI Enterprise software platform. Starting next month, members of the NVIDIA Developer Program can access NIM for free for research, development and testing on their preferred infrastructure.

40+ NIM Microservices Power Gen AI Models Across Modalities NIM containers are pre-built to speed model deployment for GPU-accelerated inference and can include NVIDIA CUDA ® software, NVIDIA Triton Inference Server ™ and NVIDIA TensorRT™-LLM software.

Over 40 NVIDIA and community models are available to experience as NIM endpoints on ai.nvidia.com , including Databricks DBRX , Google’s open model Gemma, Meta Llama 3, Microsoft Phi-3, Mistral Large, Mixtral 8x22B and Snowflake Arctic.

Developers can now access NVIDIA NIM microservices for Meta Llama 3 models from the Hugging Face AI platform. This lets developers easily access and run the Llama 3 NIM in just a few clicks using Hugging Face Inference Endpoints, powered by NVIDIA GPUs on their preferred cloud.

Enterprises can use NIM to run applications for generating text, images and video, speech and digital humans. With NVIDIA BioNeMo ™ NIM microservices for digital biology, researchers can build novel protein structures to accelerate drug discovery.

Dozens of healthcare companies are deploying NIM to power generative AI inference across a range of applications, including surgical planning, digital assistants, drug discovery and clinical trial optimization.

With new NVIDIA ACE NIM microservices , developers can easily build and operate interactive, lifelike digital humans in applications for customer service, telehealth, education, gaming and entertainment.

Hundreds of AI Ecosyst em Partners Embedding NIM Platform providers including Canonical , Red Hat , Nutanix and VMware (acquired by Broadcom) are supporting NIM on open-source KServe or enterprise solutions. AI application companies Hippocratic AI , Glean , Kinetica and Redis are also deploying NIM to power generative AI inference.

Leading AI tools and MLOps partners — including Amazon SageMaker, Microsoft Azure AI, Dataiku, DataRobot, deepset , Domino Data Lab, LangChain , Llama Index , Replicate , Run.ai, Saturn Cloud , Securiti AI and Weights & Biases — have also embedded NIM into their platforms to enable developers to build and deploy domain-specific generative AI applications with optimized inference.

Global system integrators and service delivery partners Accenture, Deloitte, Infosys, Latentview , Quantiphi , SoftServe, TCS and Wipro have created NIM competencies to help the world’s enterprises quickly develop and deploy production AI strategies.

Enterprises can run NIM-enabled applications virtually anywhere, including on NVIDIA-Certified Systems ™ from global infrastructure manufacturers Cisco, Dell Technologies , Hewlett-Packard Enterprise , Lenovo and Supermicro, as well as server manufacturers ASRock Rack , ASUS , GIGABYTE , Ingrasys , Inventec , Pegatron , QCT, Wistron and Wiwynn. NIM microservices have also been integrated into Amazon Web Services , Google Cloud , Azure and Oracle Cloud Infrastructure .

Titans of Industry Amp Up Gener ative AI With NIM Industry leaders Foxconn, Pegatron, Amdocs , Lowe’s, ServiceNow and Siemens are among the businesses using NIM for generative AI applications in manufacturing, healthcare, financial services, retail, customer service and more:

• Foxconn — the world’s largest electronics manufacturer — is using NIM in the development of domain-specific LLMs embedded into a variety of internal systems and processes in its AI factories for smart manufacturing, smart cities and smart electric vehicles.
• Pegatron — a Taiwanese electronics manufacturing company — is leveraging NIM for Project TaME, a Taiwan Mixtral of Experts model designed to advance the development of local LLMs for industries.
• Amdocs — a leading global provider of software and services to communications and media companies — is using NIM to run a customer billing LLM that significantly lowers the cost of tokens, improves accuracy by up to 30% and reduces latency by 80%, driving near real-time responses.
• Lowe’s — a FORTUNE ® 50 home improvement company — is using generative AI for a variety of use cases. For example, the retailer is leveraging NVIDIA NIM inference microservices to elevate experiences for associates and customers.
• ServiceNow — the AI platform for business transformation — announced earlier this year that it was one of the first platform providers to access NIM to enable fast, scalable and more cost-effective LLM development and deployment for its customers. NIM microservices are integrated within the Now AI multimodal model and are available to customers that have ServiceNow’s generative AI experience, Now Assist, installed.
• Siemens — a global technology company focused on industry, infrastructure, transport and healthcare — is integrating its operational technology with NIM microservices for shop floor AI workloads. It is also building an on-premises version of its Industrial Copilot for Machine Operators using NIM.

Availability Developers can experiment with NVIDIA microservices at ai.nvidia.com at no charge. Enterprises can deploy production-grade NIM microservices with NVIDIA AI Enterprise running on NVIDIA-Certified Systems and leading cloud platforms. Starting next month, members of the NVIDIA Developer Program will gain free access to NIM for research and testing.

Watch Huang’s COMPUTEX keynote to learn more about NVIDIA NIM.

About NVIDIA NVIDIA (NASDAQ: NVDA) is the world leader in accelerated computing.

For further information, contact: Anna Kiachian NVIDIA Corporation +1-650-224-9820 [email protected]

Certain statements in this press release including, but not limited to, statements as to: the benefits, impact, performance, features, and availability of NVIDIA’s products and technologies, including NVIDIA NIM, NVIDIA CUDA, NVIDIA Triton Inference Server, NVIDIA TensorRT-LLM software, NVIDIA Developer program, NVIDIA BioNeMo, NVIDIA-Certified Systems, and NVIDIA AI Enterprise; our collaborations and partnerships with third parties and the benefits and impacts thereof; third parties using or adopting our products or technologies; every enterprise looking to add generative AI to its operations; and NVIDIA NIM helping the technology industry put generative AI in reach for every organization are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

© 2024 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, BioNeMo, CUDA, NVIDIA NIM, NVIDIA Triton Inference Server and TensorRT are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability and specifications are subject to change without notice.

A photo accompanying this announcement is available at: https://www.globenewswire.com/NewsRoom/AttachmentNg/4fe99b19-66e7-4473-b9ff-f1380eae0ff8

NVIDIA NIM revolutionizes model deployment for every layer of the AI ecosystem.

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