Data transformation tools review [Updated 2023]

Einblick Content Team - September 26th, 2022

Data transformation tools help standardize data formatting, apply business logic, and otherwise play the “T” role in ETL (extract, transform, load) or ELT (extract, load, transform). These tools are used to provide a more consistent, uniform execution of data transformations, regardless of data source. Data transformation software is particularly necessary when you have a large number of data sources, multiple types of data, bring in new data consistently, and/or need to perform incremental updates. Read on to learn more about data transformation and some of the tools out there that can make your life and your team’s lives easier.

Data transformationData transformation

What is data transformation?

Data transformation is the process of transforming raw data into a format that can be loaded into a database, data warehouse, data lake, or some other data storage system so that data analysts and data scientists, or other stakeholders can then use the data for analysis. This can be done in a number of ways depending on where the data was extracted from and where it needs to be loaded into.

For instance, the raw data may be extracted as CSV files or comma-separated values, but ultimately your team wants to put the data into a relational database, which has a structure of tables and columns. Additionally, the raw data may have extraneous information or individual values may be formatted weirdly. Data transformation can help you get from the CSV files to organized and intuitive tables that the data engineers can store in your company’s relational database.

Why is data transformation important?

Raw or untransformed data is exactly what it sounds like–“raw”—it’s messy, and not ready to be consumed by data scientists or analysts. After extracting raw data (E), and before (sometimes after) loading data (L) into storage, data engineers need to transform the data into a reasonable format (T-transform) for others that may need the data in the future. Properly formatted and validated data improves data quality and protects applications from potential problems such as null values, unexpected duplicates, incorrect indexing, and incompatible formats. Some of these errors can be caught further down the pipeline during data profiling or exploratory data analysis (EDA) but it may be harder to diagnose the cause, and more laborious to correct at that point.

Data transformation benefits

Data transformation can improve the performance of data science applications. Data that is in a consistent format can be loaded faster, with less memory and less parsing overhead. Also, more data can be loaded into an application without having to reformat the data. This enables more data to be processed in parallel, meaning the application will run faster.

Data transformation can help ensure data quality. If the data loaded into the system were of low quality or had inconsistent formatting, it’s possible that software could not represent the data accurately, or worse, decisions or suggestions made off of machine learning models trained on the faulty data could have negative consequences. Data transformation tools can help ensure consistency across multiple applications by enabling consistent behavior. This will allow different applications to share configuration information and use shared schemas to avoid duplication of work in each application.

Some data science applications have built-in quality control measures that can only be leveraged when data is formatted correctly. Data quality significantly affects the outcome of the data analysis process. Poorly formatted, incomplete, inconsistent, or missing data will most likely produce errors in analysis that lead to results that are not representative or meaningful.

Data cleaning is an expected part of the data analysis process, but it can’t always correct for problems from when the data was extracted and transformed in the ETL process. Proper data transformation removes potential errors from the dataset that may lead to anomalies in later processing stages.

Data transformation benefitsData transformation benefits

What should I look for in data transformation software?

Consider the answer to these important questions before pulling the trigger on buying a data transformation solution:

  • Is the tool easy to use with the data science tool you use or are likely to adopt?
  • Does the data transformation tool support any of the default formats that you can import into our data warehousing solution?
  • Does the data transformation software support integrations with other platforms key to your business?
  • Does the tool support features crucial to the steps preparatory to the modeling of your data such as selecting and removing elements, rearranging data in various formats, cleansing duplicate values, etc.?
  • How user-friendly is the user interface? Some tools include sophisticated query language that is difficult to learn but can add significant productivity. Others include a user interface that is simple to understand and use without too much complexity, but may not be as flexible.
  • How much functionality does this tool offer versus other tools at their respective price points? And does it offer more functionality than required by your organization’s data transformation needs?

So to sum up, your data team should select and implement an application for data transformation that best meets your needs. This requires that you think through all the potential applications and how your organization collects, processes, and presents your data.

Do I have to use special tools for validating the data?

You do not have to validate the data before loading it into your data warehouse solution or converting it into another format. You can perform advanced validation for very specific and detailed requirements. For example, if your company has strict processes in place or if you just want to ensure the data is usable and high quality, you should be able to validate the data using these tools.

Here’s a short synopsis of some of the most important data transformation tools in the marketplace in 2022:


There are many tools focused very specifically on the data transformation piece for data engineers and others who may need to perform such tasks as data teams scale up. But in many cases, data engineers are working on data transformation and in the ETL pipeline, while data analysts and data scientists then have to continue from where data engineers left off to move toward creating visualizations, building machine learning models, and sharing insights with business stakeholders. As a result, there is a real need for software that can connect these two disparate parties–data engineers and data scientists.

Screenshot of Einblick canvas in live mode with three team membersScreenshot of Einblick canvas in live mode with three team members

Einblick is a collaborative data science platform focused on speeding up the path to insight for data scientists. In Einblick, you can:

  • Leverage Einblick’s uniquely collaborative canvas to share work between stakeholders from data engineers to data scientists.
  • Use the SQL dataset operator not only to connect to a SQL database, but also to create new tables with all the flexibility and data transformation capabilities of SQL at your disposal.
  • Use cells to modularize and re-use code snippets for mundane tasks.
  • Use Python cells for further data cleaning and data preparation work, as well as exploratory data analysis.

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Einblick pricing

Einblick has a free tier, as well as several other premium options available, so you can pick the best solution for you and your team. For general pricing information, check out Einblick’s pricing page.


Apache Airflow is an open source workflow management platform. You can automate and schedule different processes, such as upload and organize your data into meaningful visualizations, drill into the details of your data, identify trends and anomalies in your data and produce dashboards to visualize workflows, which are structured as DAGs (directed acyclic graphs) and schedule data pipelines or workflows. Airflow has various operators to help you accomplish different tasks related to the ETL pipeline.

Edit multiple charts

As your DAGs get bigger, it can be important to group them together. Airflow has a feature called TaskGroups to help you visualize your different groupings of tasks in the same DAG. This can help to break things down into more manageable pieces as the DAG gets more complex.

Custom data pipeline

Airflow can offer a very simple data pipeline with only two stages (input and output).Some data engineers may need more complex ETL pipelines or a lot of rules,but still, it’s useful to consult, because it’s really easy to set up and it can handle some data cleaning tasks. With Airflow, you can create custom data pipelines as you need. You can find examples of custom data pipelines on Airflow’s website. Airflow is open-source, and has an active community of contributors.

Airflow pricing

You can read more about Apache Airflow and their open source community online.


CloverDX is a powerful, yet easy-to-use platform that enables organizations to easily capture real-time data and transform it into actionable analytics and insights. The CloverDX platform is based on the Java programming language and utilizes an easy-to-use visual user interface. In CloverDX, you can build Data Apps, which let you publish a data integration job as an application with a web-based user interface.

CloverDX uses the CloverDX Transformation Language (CTL), which is a high-level language, to create a level of abstraction from data transformation. In CloverDX, a Transformation is a piece of code that defines how the input data is being changed or modified before it is output.

CloverDX pricing

CloverDX started as an open-source ETL platform called CloverETL, but is now a commercial product. That being said, CloverDX was built on many open source libraries. For general pricing information, visit CloverDX’s pricing page. is a no-code data integration tool, designed for teams. You can use to transform your data as part of your ETL pipeline, as well as create dashboards and reports, and more. Many of the reports in can be customized to include other data sources such as databases or APIs. also has a whole suite of connectors that can be used with other platforms, including Slack, Trello, Microsoft, and more. aims to automate your data integration workflows by setting up schedules to import data. Then any reports and dashboards will update automatically. can pull information out of various CRMs or other relevant data sources and export it into Google Sheets, Microsoft Excel, or BigQuery. pricing

For general pricing information, visit’s pricing page.


Dataform is a data transformation tool that allows users to transform and integrate data from multiple sources. It was designed for handling complex data that is already loaded in a data warehouse. It allows the user to transform, merge and polish data in different ways. Dataform is highly configurable and easy-to-use resulting in a fast, efficient and reliable tool.

Dataform predominantly works with SQL commands in your data warehouse. Using Dataform, you can create new tables and views, add documentation to those tables and views, define assertions to test data quality, and more. Dataform is going against the grain of ETL (extraction, transformation, and loading) to focus on ELT (extraction, loading, transformation) with the argument that if you transform before loading in the data, data engineers have to transform the data based on predictions of how the data is going to be used. If, however, teams can load the data before transforming the data, then perhaps the transformations can be more focused and efficient for the team’s use-cases.

Dataform pricing

Dataform is now part of Google Cloud. For general pricing information, visit Dataform’s pricing page.


Datameer is a powerful multi-persona SaaS data transformation platform for Snowflake. It offers a highly scalable platform for no-code or low-code data transformation, as well as on-demand and ad-hoc analysis and reporting. Datameer is a tool built specifically for Snowflake data.

Datameer connectors

The connectors are the portals where data sources are introduced. Customers could quickly and easily import data sources into their Datameer projects, and choose the connector type that matches their requirements. Any data that is part of the user’s Snowflake source can be used in a Datameer Project. You can also upload CSV files into Datameer, as Datameer datasets.

Once in Datameer, you can use Datameer recipes to chain together different transformations. The different data transformation operations resemble Excel formulas, such as ABS or CONCAT. Datameer can also export data into a CSV, but mainly into your Snowflake instance.

Datameer example

For example, after logging into Datameer, you can create a new project, add your relevant tables and data to the Project, either by connecting to Snowflake or uploading a CSV, then you can perform a few data transformation operations on your data. You could use the join operation and then the aggregate operation–let’s say if you are interested in understanding the statistics for a particular sport season. Perhaps you have a table of game results, a table of teams, and a table of team rosters. You can use various data transformations, such as joins and aggregate, to create a new table that will then be used for actual data analysis. Then you can deploy that table to Snowflake.

Datameer pricing

For general pricing information, visit Datameer’s pricing page.

Data transformation softwareData transformation software


dbt is a powerful data transformation workflow tool. Dbt allows engineers to work with their data in the warehouses they are already using via simple select statements. Dbt simplifies the process for engineers by turning the select statements into tables and views. Dbt is both a compiler and a runner. Users can write dbt code and then use dbt in the command line. Dbt will then turn the code into SQL, and execute the various SQL queries directly in the data warehouse of choice.

dbt feature highlights

  • Dbt uses modular data modeling so that it is easier to understand and continue individual work from different teammates
  • You can define transformations in native SQL syntax
  • Dbt supports many different data platforms via a dedicated adapter plugin so that work can remain high-level for data engineers

dbt pricing

For general pricing information, visit dbt’s pricing page.


EasyMorph is another data preparation tool that focuses on no-code data transformations. EasyMorph combines data preparation and automation in visual workflows that can have loops, conditional branches, and subroutines. You can create complex workflows using graphical elements and connectors which connect your existing workflows, as well as create new workflows using the drag-and-drop editor.

EasyMorph Desktop is available for download on Windows only. You can import data from a variety of supported data formats, including CSV, Excel spreadsheets, and XML files. Additionally, you can connect to data stored in databases like SQL Server, Azure SQL Database, Oracle, Snowflake, and Google BigQuery. If you are familiar with Microsoft Office, the format and look of EasyMorph may be familiar to you.

In EasyMorph, you can perform some data transformations, which EasyMorph’s documentation may refer to as “actions.” There is a shortlist of data transformations available, including aggregate, append, lookup, merge, and replace.

EasyMorph pricing

For general pricing information, visit EasyMorph’s pricing page.

Hevo Data

Hevo Data is an end-to-end data pipeline platform, which provides two main tools for the ETL/ELT pipeline: Hevo Pipeline and Hevo Activate. With over 150+ integrations, Hevo Data securely handles data integration and monitors the health of your company’s data pipeline.

The biggest problem with database migrations is that it sometimes requires time, money and effort when the database needs to be upgraded and so on.

Semiautomatic database connections

With Hevo Data, can easily connect your database to a data warehouse of your choice with just a few clicks, and no-code. Hevo will automatically create the destination schema of your data based on the source, and will sync your destination as your source data changes or updates. Hevo Data will detect any changes in the automated incoming data, and then replicate those changes in your destination schema.


With this feature developers can make sure that their API can communicate with any service, not only with just one known provider or platform. You can consult Hevo’s website to learn how to configure REST API as a Source in Hevo.

Hevo Data pricing

For general pricing information, visit Hevo Data’s pricing page.

IBM InfoSphere DataStage

IBM InfoSphere DataStage is a data integration tool focused on moving and transforming data. InfoSphere DataStage supports ETL and ELT workflows, while focusing on the move to modern cloud-based architecture, for improved scalability and elasticity. Since DataStage is part of the larger IBM ecosystem, DataStage will help to augment any other IBM services or products you may already be using.

IBM’s InfoSphere DataStage is part of a larger data fabric architecture and solution called the Cloud Pak for Data. The data fabric architecture helps manage some of the difficulties of the modern data industry–data comes from a myriad of sources, and there are repetitive processes.

IBM InfoSphere Master Data Management

For example, IBM’s InfoSphere Master Data Management product helps to reconcile differences in your data, and ensure that your data is up-to-date and high quality. The InfoSphere Master Data Management tool is also a part of the IBM Cloud Pak for Data platform.

IBM InfoSphere DataStage pricing

For general pricing information, visit IBM DataStage’s pricing page.

Informatica PowerCenter

Informatica PowerCenter is a cloud-based metadata-driven enterprise data integration platform that helps businesses with the life cycle of data integration, such as the ETL or ELT processes. With Informatica PowerCenter your business intelligence and IT teams can use role-based tools and processes to ensure that the right data is delivered with expediency. The platform is code-less, so users can use pre-built transformations to work with their raw data. You can also host PowerCenter on Amazon Web Services (AWS) or on Microsoft Azure.

PowerCenter gives users an environment where they can extract data from various sources, transform the data, and load the transformed data into files or other destinations. PowerCenter includes the Informatica domain, PowerCenter repository, domain configuration, PowerCenter Client, and more. The PowerCenter Client applications allows you to use tools to manage the PowerCenter repository, design mappings, and sessions to work with your data. Within the PowerCenter Client you can use different tools, like the PowerCenter Designer.

Informatica PowerCenter Designer

With the PowerCenter Designer, you gain access to a variety of tools that can help you analyze the sources of your data, create targets for your data. In terms of transformation, you can use the Transformation Developer tool to create and execute different data transformations.

Informatica PowerCenter pricing

For general pricing information, visit Informatica’s pricing page.


Matillion provides enterprises with a cloud-native data integration solution. Matillion has a complete suite of data transformation and analytics functions, enabling organizations to transform raw data into meaningful information. The Matillion ETL tool uses the cloud to go from raw data to meaningful information for analysis quickly.

Despite its flexibility, Matillion is easy to learn and use. It offers:

  • A data transformation pipeline that can be actively customized and enhanced
  • An intuitive user interface that makes it easy to create transformations using a drag-and-drop visual interface
  • Version control
  • Validation and data preview as you build different jobs in the ETL/ELT process

Matillion Functions

Matillion has a lot of functionality to offer in the ETL space. In particular, when searching for a data transformation solution, Matillion ETL offers a variety of data transformation functionality. For example, you can

  • Use the aggregate component to group together different input rows into one output row.
  • Use the distinct component to pass only unique input records to the next component or part of the job.
  • Transpose columns to unpivot data.
  • Use the rename component to change names of fields within the data flow.

Matillion pricing

For general pricing information, visit Matillion’s pricing page.

Data transformation toolsData transformation tools

Pentaho Data Integration

Pentaho Data Integration (PDI) provides the Extract, Transform, and Load (ETL) capabilities that facilitates the process of capturing, transforming, loading, and consolidating data so that it is accessible and relevant to different users and for different purposes. You can use PDI to migrate data between databases and applications, transform data as part of data cleansing, and more.

PDI Server-based Utilities

This type of utilities are ideal for unattended processing of data because it is a server-based software, which means it has many processing threads and should be able to handle large volumes of data without significant CPU impact to the development machines of users. It provides an interface for programs to access the data and provide programmatic access to the results of processing done by the utility.

PDI Server-based Reporter

You can use the Schedules page of the Pentaho User Console to schedule reports to be run and delivered at regular intervals or on certain dates and times. You can also use the Pentaho Report Designer to create data reports as you need, after you have connected to a data source, loaded in your data, and arranged the data elements for your purposes.

Pentaho Data Integration pricing

Pentaho became part of Hitachi Data Systems in 2015. For general pricing information, visit PDI’s pricing page.


Talend has been a leader in the data space and offers several different products. Talend Data Fabric provides a single suite of cloud apps for data integration and data integrity to help enterprises collect, govern, transform, and act on customer data. From the Talend Data Fabric, you can manage data integration, application API integration, and data integrity and data quality, all powered by Talend Trust Score. The core idea is combining the steps of data integration with data integrity in one platform.

With regard to data transformation, you can add the tMap component to your Job in the Talend interface. The tMap component can then handle joins, transformations, and more. After you are done with transforming your data, you can output your data to a target like a MySQL table.

Talend pricing

For general pricing information, visit Talend’s pricing page.


The Trifacta Designer Cloud platform leverages the multi-workload processing features of Hadoop to scale data transformation processing seamlessly from small to big data through Spark and MapReduce. Trifacta provides support for any cloud data warehouse or cloud data lake or data lakehouse. Whatever existing data storage solution you are using for your team will work with Trifacta.

Trifacta helps you automate data transformation steps, which you can save as recipes to be included as part of automated data pipelines. Trifacta uses active data profiling to notify users of any potential data quality issues, and uses an AI-assisted process to suggest data transformations, which can then be applied just by clicking and dragging.

Trifacta pricing

For general pricing information about Trifacta’s Designer Cloud, visit their pricing page.

Frequently asked questions

Some of the most commonly used data transformation techniques are:

  • Extract, Transform and Load (ETL)
  • Data Warehousing
  • Data Mining
  • Business Intelligence
  • Data Visualization

Data transformation is the process of converting data from one format or structure to another. This can be done for a variety of reasons, such as to make it easier to analyze or to convert it to a format that can be more easily processed by another system.

Data transformation is important because it allows organizations to convert their data into a format that can be more easily analyzed and processed. This can help them to better understand their business operations and make better decisions.

Some common data transformation tasks include:

  • Data Cleaning: This involves removing invalid, duplicate, or incomplete data from a dataset.
  • Data Normalization: This involves converting data into a consistent format.
  • Data Transformation: This involves converting data from one format to another.

Some common data transformation tools include:

  • Extract, Transform and Load (ETL) tools: These tools are designed to help organizations extract data from multiple sources, transform it into a consistent format, and load it into a destination system.
  • Data Warehousing tools: These tools are designed to help organizations store and manage large amounts of data.
  • Data Mining tools: These tools are designed to help organizations analyze large amounts of data to find trends and patterns.
  • Business Intelligence tools: These tools are designed to help organizations make better decisions by providing them with insights derived from data analysis.
  • Data Visualization tools: These tools are designed to help organizations understand data by representing it in a visually-friendly manner.

There are a few things you can do to improve your data transformation process:

  • Understand your data: Take the time to understand the structure, quality, and content of your source data. This will help you choose the right transformation techniques for your project.
  • Choose the right tools: Select the tools that best fit your needs and know how to use them effectively.
  • Automate where possible: Automating repetitive tasks can save you time and improve accuracy.
  • Test, test, test: Always test your transformation process before deploying it to production. This will help you catch errors and ensure that the process works as expected.

Some common errors in data transformation include:

  • Incorrect data type conversion: Data type conversion errors can occur when data is converted from one type to another (e.g., from string to integer). This can cause values to be lost or misinterpreted.
  • Invalid data values: Invalid data values can occur when data does not meet the requirements of the destination system (e.g., values that are out of range). This can cause the transformation process to fail or produce incorrect results.
  • Duplicate data: Duplicate data can occur when the same data is extracted from multiple sources or when data is transformed multiple times. This can cause problems with downstream processes, such as duplicate records in a database table.
  • Missing data: Missing data can occur when data is not extracted from all sources or when required fields are not populated during transformation. This can cause problems with downstream processes, such as missing information in a report.


Einblick is an AI-native data science platform that provides data teams with an agile workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit and follow us on LinkedIn and Twitter.

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