Data Transformation Tools: Top 10 Picks & Their Capabilities in 2025

Updated December 03rd, 2024
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Data transformation tools such as AWS Glue, Azure Data Factory, Informatica, dbt, Denodo, Domo, FME, Hevo, Matillion, Nexla are essential components of the modern data ecosystem, enabling organizations to process, refine, and format raw data into usable formats for analytics, reporting, and business intelligence.
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These tools extract data from various sources, cleanse it, and transform it to ensure consistency and compatibility before loading it into data warehouses or other systems.

By automating tasks like filtering, summarizing, and reformatting data, they eliminate manual effort, reduce errors, and enhance scalability.

Whether used for real-time processing or batch operations, data transformation tools are critical for maintaining data quality, driving efficiency, and enabling informed decision-making across diverse use cases.

Data transformation tools extract data from various sources and formats, and process and refine that data to load it into data warehouses or other tools in the data tech stack. The primary objective of these tools is to help data teams maintain and update their databases with relevant, useful data crucial for analytics and BI.

Data transformation includes procedures such as filtering, summarizing, and formatting data. It can be done manually, automated with open-source and commercial tools, or handled with a combination of the two. A variety of solutions are available that streamline the process of transformation to make it more manageable and scalable.

As per 2024 Statista survey on Digital transformation spending worldwide 2017-2027, by the end of 2024, the global spending on digital transformation is expected to reach USD 2.5 trillion, with forecasts predicting it will rise to USD 3.9 trillion by 2027.

In this article, we will explore the ten most popular data transformation tools in 2025. These tools have been featured in popular review portals like Gartner.

Table of contents #

  1. Here are the ten most popular data transformation tools in 2025
  2. AWS Glue
  3. Azure Data Factory
  4. dbt
  5. Denodo Platform
  6. Domo
  7. FME
  8. Hevo
  9. Matillion
  10. Nexla
  11. How organizations making the most out of their data using Atlan
  12. FAQs About Data Transformation Tools
  13. Data transformation tools: Related reads

To help you choose the right tool that fulfills your needs, here’s a list of the most popular data transformation tools and what they can do for you. Let’s explore each one of them in detail.

  1. AWS Glue
  2. Azure Data Transformation
  3. Cloud Data Integration for Cloud ETL and ELT by Informatica
  4. dbt
  5. Denodo Platform
  6. Domo
  7. FME
  8. Hevo Data
  9. Matillion
  10. Nexla

AWS Glue #

AWS Glue is a data transformation tool that helps find, process, and merge data for further analytics.

Since it’s part of the AWS suite of services, it integrates well with other AWS offerings, such as Amazon S3, Amazon RDS, Amazon Redshift, and Amazon Athena.

Moreover, it is serverless, so you needn’t worry about your infrastructure overhead. Pay only for the resources you consume.

What are some of the main data transformation capabilities of AWS Glue? #


  • AWS Glue lets you merge data across multiple data stores and oversee thousands of ETL processes or workflows at once with a unified catalog.
  • You can use AWS Glue Studio to set up and manage various ETL jobs visually, with a drag-and-drop editor.
  • When you use AWS Glue for data from various sources, it automatically identifies the data format and suggests appropriate schemas.

AWS Glue resources

AWS Glue details | AWS Glue in-depth | AWS Glue documentation

Azure Data Factory #

Azure Data Factory helps you cleanse, merge, and format enterprise data at scale.

It is a serverless tool that lets you visually integrate data sources using 90+ built-in connectors. So, while you can write your own code, you can also choose to design ETL or ELT pipelines and let Data Factory generate the code automatically.

Azure Data Factory has been certified by top compliance bodies such as HIPAA and HITECH, ISO/IEC 27001, and CSA STAR.

What are some of the main data transformation capabilities of Azure Data Factory? #


  • Azure Data Factory offers built-in Git and CI/CD support.
  • It uses a pay-as-you-go model, making it easier for you to scale up or down as per your requirements. There are no upfront costs involved.
  • The code-free environment enables citizen integrators to drive analytics and BI without engineering or IT support.
  • It provides a data transformation layer that works across your digital transformation initiatives. You can transform faster and more intelligently with intent-driven mapping. It will automate copy activities.

Azure Data Transformation resources

About Azure Data Transformation | Azure Data Transformation demos | Azure Data Transformation pricing

Cloud Data Integration for Cloud ETL and ELT by Informatica #


Informatica offers high-quality, cost-effective solutions for your data transformation needs.

With Informatica’s cloud data integration suite, you can perform mass data ingestion across sources, integrate app data in real time, and build pipelines without worrying about the overall infrastructure.

The solution can handle ETL and ELT for multi-cloud environments and comes with a free 30-day trial.

What are some of the main data transformation capabilities of Cloud Data Integration for Cloud ETL and ELT? #


  • You can integrate data with a wide range of processes — ETL, ELT, Spark, or a serverless option.
  • The pricing depends on your consumption, making it easier for you to scale your cloud up or down as per the demand.
  • The solution integrates with the various tools in your data stack, regardless of whether they’re on-premise or SaaS.
  • You don’t need advanced coding knowledge to use the solution as it’s equipped with 100+ pre-built templates for setting up data pipelines.

Informatica resources

Solution brief | Faster, Simpler, More Cost-Effective Cloud Data Integration for Analytics [Ebook]

dbt #

dbt helps you transform, test, and document data from various sources such as cloud warehouses, data lakes, and lake houses. In addition, it offers support adapters for each technology — Postgres, Redshift, Bigquery, SQL Server, and more.

dbt only does the T in ELT, so it doesn’t extract or load data, just transforms the data already loaded into your warehouse.

dbt offers version control, testing, logging, and alerting to simplify governance. It is compliant with SOC2 Type II, ISO 27001:2013, ISO 27701:2019, GDPR, PCI, and HIPAA.

What are some of the main data transformation capabilities of dbt? #


  • You can write SQL SELECT statements to transform the data in your warehouse.
  • dbt auto-generates dependency graphs and dynamic data dictionaries.
  • dbt logs and assets are regarding the transformations you run. They don’t store or reveal any information about the actual data from the warehouse. So, you have complete control and ownership of your data.

dbt resources

Documentation | Training | What is dbt?

Denodo Platform #

Denodo Platform is a data transformation tool built for the logical data fabric, which includes an active data catalog, smart querying, automated cloud infrastructure management, and more.

The platform lets you connect disparate data from various sources, set up transformations as per your use cases, and prepare data in real time.

It also offers custom training programs for each role within your data teams, such as architects, developers, admins, and business users.

What are some of the main data transformation capabilities of the Denodo Platform? #


  • Denodo Platform covers the needs of everyone (from business to IT stakeholders) and has an easy-to-use, web-based user interface.
  • It supports OAuth 2.0, SAML, OpenAPI, OData 4, GraphQL, and other cloud standards for interoperability in multi-cloud environments.
  • The platform automates infrastructure management and offers PaaS support.

Denodo Platform resources

Denodo Platform demo | Denodo Standard datasheet

Domo #

Domo lets you integrate data from various sources with 1000+ pre-built cloud connectors to cut engineering costs and save time. It also offers on-premise connectors and connections to proprietary systems.

After integrating data, you can transform and query it, set up pipelines, and use the data to drive data science, analytics, and BI. Domo is compliant with GDPR, HIPAA, SOC 1/2, and ISO standards.

What are some of the main data transformation capabilities of Domo? #


  • Domo offers magic ETL — which lets you visually define and sequence operations with a simple drag-and-drop interface.
  • You can use MySQL or Redshift expressions to build your data pipelines.
  • Domo supports SSO and multi-factor authentication. And it also provides you with complete activity logs for security audits.

Domo resources

Documentation | Product guide | Building data integrations on a modern BI platform

FME #

FMEis a data transformation tool that offers support for geospatial data. FME lets you discover, profile, and map data so that you can set up data workflows.

FME offers several transformers to extract HTML, update or delete databases, connect with various data sources, and more. These transformers act as the building blocks of your workflow and let you modify data as per your needs.

What are some of the main data transformation capabilities of FME? #


  • Without writing any scripts, you can use drag-and-drop transformers to build your workflows.
  • You can also insert your own snippets of code (Python, R, or JavaScript) in the data transformation script.
  • FME supports dataflows for on-premise, cloud-based, and mobile applications.

FME Resources

FME Support | Documentation

Hevo #

Hevo helps you set up data transformation pipelines within minutes without any coding.

Hevo supports 100+ ready-to-use integrations for databases, cloud-based applications, streaming services, and more. Moreover, it can handle millions of records per minute without latency, making it easier for you to scale your pipelines as per your requirements.

Setting up a pipeline is straightforward as once you choose your data source, add your credentials, and choose the destination warehouse to load data, Hevo builds the data flows automatically.

What are some of the main data transformation capabilities of Hevo? #


  • The interface is no-code and intuitive, so anyone can build data pipelines, which removes the engineering bottleneck and saves time.
  • Hevo handles all pipeline operations, saving infrastructure setup and maintenance costs.
  • Hevo supports reverse ETL to send warehouse data to any business application.

Hevo resources

Hevo Data homepage | Hevo Data documentations | Hevo Data video

Matillion #

Matillion comes equipped with pre-built data source connectors for on-premises and cloud databases, NoSQL sources, APIs, business applications, and more. You can also set up custom connectors to simplify data extraction from various sources.

Matillion provides a drag-and-drop interface that makes it easy to create complex transformations without needing any coding skills.

Moreover, Matillion uses the NIST framework for data confidentiality, integrity, and availability. It’s also compliant with industry standards and regulations such as SOC Type II, HIPAA, CSA STAR, GDPR, and CCPA.

What are some of the main data transformation capabilities of Matillion? #


  • You can automate and schedule pipeline-related jobs and also automatically generate documentation for these processes.
  • You maintain control and ownership of your data at all times.
  • Matillion supports reverse ETL, so you can write your transformed data back out to your data warehouse or lake.

You can choose between hourly pricing or pay-as-you-go models, or even have an enterprise contract.

Matillion resources

Matillion demo

Nexla #

Nexla makes it easy to integrate data from anywhere and transform it into a ready-to-use format.

Nexla enables self-service data preparation, wherein you don’t have to rely on engineering to prepare data pipelines or track lineage because of Nexla’s no-code interface. It offers a rich library of transformation functions to help even business users handle data transformation.

What are some of the main data transformation capabilities of Nexla? #


  • You can see what’s happening to your data at a glance — pipelines, transformations, integrations, and more.
  • Nexla offers automated and continuous schema management, wherein you can detect schemas and observe the changes made to the schemas and their subsets/supersets.
  • Nexla offers automated versioning and logs so that you know how data sets have changed and who handled those changes. This makes compliance a breeze and your data more trustworthy.

Nexla resources

Data transformation with Nexla | Press release | Help center

A Gartner article states that the demand for real-time data processing is on the rise, with 58% of businesses implementing streaming data transformation tools to gain immediate insights and enhance decision-making.

Also, read → Modern Data Stack: The path to a scalable hybrid cloud solution | Master data management

How organizations making the most out of their data using Atlan #

The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:

  1. Automatic cataloging of the entire technology, data, and AI ecosystem
  2. Enabling the data ecosystem AI and automation first
  3. Prioritizing data democratization and self-service

These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”

For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.

A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.

Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes #


  • Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
  • After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
  • Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.

Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.

FAQs About Data Transformation Tools #

1. What are data transformation tools? #


Data transformation tools are software solutions designed to extract, transform, and load (ETL) data from various sources. They help refine raw data into structured formats for analytics and business intelligence, enhancing the usability and relevance of data.

2. How do data transformation tools work? #


These tools automate the ETL process, extracting data from disparate sources, transforming it through filtering, summarizing, and formatting, and loading it into data warehouses or other platforms. This streamlines data management and ensures consistent data quality.

3. Which tools are best for automating ETL processes? #


Top tools for automating ETL processes in 2025 include AWS Glue, Azure Data Factory, and Talend. These platforms offer powerful automation features, reducing manual intervention and improving data processing efficiency.

4. What are the benefits of using data transformation tools? #


Key benefits include improved data quality, faster data processing, enhanced scalability, and seamless integration with other systems. They enable businesses to make data-driven decisions by providing accurate and timely data.

5. How do data transformation tools improve data quality? #


By automating data validation, cleansing, and transformation, these tools reduce errors and inconsistencies. They ensure data is accurate, complete, and formatted for analysis, leading to better insights and decision-making.

6. What are the top data transformation tools in 2025? #


Leading tools in 2025 include AWS Glue, Azure Data Factory, Talend, and Informatica. Each offers unique features like real-time data processing, cloud compatibility, and advanced transformation capabilities to suit diverse business needs.

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