12 Best Data Transformation Tools: Top Features & Benefits

CEO, Portable

The sheer amount of data available today can make extracting valuable insights difficult.

That's where (good) data transformation tools come in. They transform data to help you make informed decisions.

Proper data preparation can result in an edge over your competitors.

In this article, we've listed 12 of the best data transformation tools on the market. We've also shared popular use cases and must-have features.

What Is Data Transformation?

  • Data transformation converts data from its original format to a new one. This task is typically done for data science and analysis.
  • The process converts raw data into actionable insights. Transformation is necessary when working with multiple data sources to manage large data sets effectively.

Data transformation includes essential functions like:

  • Data cleaning prepares raw data for analysis by removing errors, inconsistencies, and outliers.
  • Data filtering chooses data from a big data set for viewing or analysis. 
  • Data restructuring transforms a data set's structure from one form to another.
  • Data aggregating summarizes data from many sources. This helps identify patterns, trends, and relationships in data. 

Common Data Transformation Use Cases

  • Healthcare. Researchers use patient data to study disease patterns and develop therapies. But the caveat: they need anonymous data. Data transformation can strip private details from data sets and sanitize them for research.
  • Finance. Financial institutions pull from several data sources. Data transformation combines data from these systems into a single format, making it easier to compare and analyze.
  • Retail. E-commerce data is spread across channels like online stores, physical stores, and mobile apps. Data transformation tools help retailers merge data into a single hub.
  • Education. Data transformation can help standardize information from enrollment, student information, and learning management systems.
  • Government. Government agencies often have data in different silos and systems. So by transforming and integrating the data, they can analyze it. This helps them gain insights into how to provide public services. 
  • Media and entertainment. Media companies get tons of data from social media, streaming platforms, ad networks, and more. Transforming and combining the data can help them identify user trends and preferences. 

What to Look for in a Data Transformation Tool

  • Integrations and data source connectors. When choosing a tool, ensure your solution pulls from the key data sources you need.
  • Speed. If you're transforming large volumes of data, look for a tool that transforms data quickly. 
  • Scalability. If you're handling a growing volume of data, you'll want a tool that can handle the increase in workload.
  • Ease of use. If your team doesn't have professional data engineers or analysts, look for low-code or no-code tools. And if coding is required, get a tool that has an easy-to-use interface.
  • Data quality controls. The data transformation process can introduce errors and inconsistencies. The tool you choose should be capable of ensuring the data is accurate, complete, and reliable. 

Top Data Transformation Tools for 2023

1) Portable

Portable is the best ETL/ELT solution for teams working with long-tail data sources.

Most companies offer a handful of connectors from the same usual sources. But Portable provides connectors for 350+ lesser-known sources.

You don't have to know any code to use it, and it's simple to start with. 


  • Handles data extraction, in-flight transformations, and loading. Portable's transformations include defining data types, creating schemas, and ensuring joining keys exist.
  • Custom data source connectors are created within a few hours or days.
  • No cost for ETL connector maintenance and upkeep.
  • Offers numerous data connectors for data warehouses like SnowflakeGoogle BigQueryAmazon Redshift, and PostgreSQL
  • Free plan for manual data workflows. The paid plan applies exclusively to automated data flows.

Best for: Portable is ideal if you're looking extract data from multiple data sources. This gives you the time to focus on extracting insights from the data rather than building and managing data pipelines.

2) Hevo

Hevo lets you create a data transformation pipeline within minutes -- no coding required. It has 150+ pre-built integrations, including plenty of SaaS apps and databases. 


  • No-code (for those who don't code) and fully-coded Python transformation options. 
  • Easy-to-navigate data visualization dashboards. 
  • Effortlessly lets you create a data pipeline without code. Connecting your data source to a destination warehouse is fast and easy.
  • Highly scalable. Hevo can handle millions of records per minute with no latency.
  • Alerts for failed workflows.

Best for: Non-coders who want an intuitive low-code interface to build pipelines with.

3) dbt

dbt is an open-source, command-line tool for transforming data. It runs SQL transformations in your data platform (data warehouse, data lakes, database, or query engine). 


  • Excellent testing capabilities to ensure transformation accuracy and reliability.
  • Tools for scheduling, CI/CD, organizing, etc. 
  • Produces trusted data sets to support ML modeling, operational workflows, and reporting. 
  • "Ref function" allows engineers to reference one data model within another. 
  • Team collaboration and version control feature for helping teams manage data pipelines.
  • Works with cloud-based data warehouses like BigQuery, Snowflake, and Redshift.

Best for: Data engineers who want to perform SQL transformations.

4) EasyMorph

EasyMorph is another great tool for non-technical users. You can retrieve data from anywhere and automate complex transformations without writing a single line of code.  


  • Comes with over 200 built-in actions and functions to automate and transform data without needing SQL or custom scripts.
  • Automates ETL processes using visual workflows and removes repetitive and complex work. This workflow gives you the time to focus on data analysis. 
  • The data catalog feature lets you collect data from cloud applications, emails, remote folders, web API endpoints, and more. 
  • Team members can access data from any remote location. 
  • Attractive data visualizations for non-technical stakeholders.

Best for: Non-technical business users.

5) Pentaho

Pentaho is an open-source ETL data integration tool. Organizations use it for data blending, transforming, migration, and cleansing of large data sets. 


  • Supports complex transformation jobs. Also comes with samples that show you how to transform data.
  • Lets you create data integration workflows using a visual drag-and-drop interface. The visual representation helps you understand complex ETL processes. 
  • While geared towards a technical audience, it's a clickable, low-code solution.
  • Has in-house data and files storage repository. 
  • Excellent data profiling and data quality capabilities

Best for: Great for organizations that want to process a large volume of data using a user-friendly way.

6) Matillion

Matillion is an open-source ETL tool that both beginners and technical experts love. It has over 100+ connectors and seamlessly integrates with business intelligence and data analytics tools. 


  • User-friendly interface with drag-and-drop features to create complex transformations. Also allows for data transformation using SQL. 
  • Can automate and schedule pipeline-related jobs.
  • Has over 100+ connectors to extract, transform, and load data from various sources. Also, has the option to create custom connectors for different applications.
  • Access technical support at no extra cost.

Best for: Matillion is great for enterprises and medium-sized companies with data teams with varied expertise.

7) Dataform

Dataform is an open-source ETL platform that businesses use to manage data in data warehousing. It works with solutions like Redshift, Snowflake, Panoply, and BigQuery.

It's more user-friendly than dbt -- but it still requires knowing SQL. 


  • Intuitive integrated development environment (IDE) for arranging tables, dependencies, and views.
  • Has version control options for your data workflows, allowing you to inspect changes before committing. And if something is wrong, you can revert to a previous version without touching the command line. 
  • Includes a "ref function" that allows data engineers to create dependencies between tables and data sources. This will enable you to spend more time analyzing data and less time managing data infrastructure.

Best for: Data analysts and engineers who can create custom code for data transformations.

8) Talend

Talend is an open-source data integration platform that lets you perform ETL tasks and cloud/on-premises jobs using Spark, Hadoop, and NoSQL databases. 


  • Can handle both simple and complex transformations. 
  • tMap component that handles joins and transformations.
  • Integrates with cloud service providers, data warehouses, and analytics platforms, including Amazon Web Services (AWS), Google Cloud Platform, Snowflake, etc. 
  • Combines data management, data integration, and data integrity in one platform.
  • Easy and intuitive GUI interface that can visualize data pipelines.

Best for: Larger enterprises that have seasoned experts and big data teams.

9) Trifacta

Trifacta's Designer Cloud is a data wrangling and visual representation platform that helps engineers clean and transform unstructured data. It also allows you to create pipelines of your choice using either Spark, SQL, Python, or dbt. 


  • Helps visually represent user data so they can manipulate it using algorithms with advanced matching capabilities, cleansing functions, and more.
  • AI-powered self-service approach to evaluate, validate, and accelerate data transformations. 
  • Supported by major cloud providers like Google Cloud, AWS, and Microsoft Azure.

Best for: Serious technical users who need a serious data wrangling and transformation solution.

10) Informatica

Informatica is a data management cloud tool for transforming data on hybrid and cloud infrastructures. 


  • Powerful transformation capabilities and the ability to scale and handle large amounts of data.
  • Integrates with traditional databases and other applications to convert data sources in real-time.
  • Intuitive user interface
  • Doesn't need advanced coding knowledge to set up data pipelines, thanks to the 100+ templates. You can map data formats using pre-built transformations. 
  • Built-in support for various data sources like SQL Server, Oracle, and Teradata.

Best for: Popularly for verticals like healthcare, finance, and banking, thanks to the pre-built libraries for most transformations in these industries.

11) Airflow

Apache Airflow is a versatile data integration platform that helps businesses to automate and manage the execution of their data pipelines


  • An open-source platform that lets you schedule and automate ETL workflows using Python for SQL. 
  • Visualization tools like graphs, tree views, code views, and Gantt charts for visualizing the scheduling and execution of pipelines. 
  • Allows you to create data pipelines with unlimited complexity.

Best for: Handling large and complex data workflows.

12) Datameer

Datameer works specifically with Snowflake, so it's excellent for companies already using Snowflake's services.

It can transform your datasets using no code, SQL, or both. The platform covers the entire data life cycle journey. It helps with discovery, transformation, deployment, and documentation -- all within Snowflake.


  • Has a Google-like search function to scan its database. 
  • It's heavily documented and offers video walkthroughs for common analytic use cases. 
  • Makes multi-stage transformations more intuitive with visual charts.
  • Lets you manage metadata such as tags, descriptions, and properties and provides robust data lineage and audit trails.
  • It can be used for analytics, machine learning, and reporting.

Best for: Non-coding data analysts who use Snowflake

The Final Word on Data Transformation Tools

Choosing the right transformation tool is a matter of identifying your business goal and the resources you have. You'll also need to strike a balance between your budget and your data team's capabilities.

But in the end, whether you're a data engineer, business owner, or anyone who works with data, the right tool can help you transform raw data into actionable insights.

And if you want to leverage insights from data from (over 350+) long-tail applications, try Portable for free.