7 Best Data Transformation Tools in 2023 (Pros, Cons, Best for)

In this article, we’ll explore the 7 best tools on the market for data transformations. Each tool will be evaluated with Pros, Cons, and a clear decision for who this tool is best for.

Are you in a hurry? No worries, here’s the TL;DR of the best tools and who are they ideal for:

1. Keboola: Best overall. Covers all transformation use cases from no-code for business experts, to low-code for engineers (in SQL, Python, R, and Julia), as well as all dbt transformations.
2. dbt: The analytics engineer who likes to use developer practices in SQL-driven transformations within their data warehouse.
3. Matillion: IT users at small- to medium-sized organizations who would like a simple but powerful tool to run their ETL processes.
4. Trifacta: Enterprise users who’d like to transform their data with a solution similar to (but more powerful than) Excell.
5. Informatica: A team of data engineers at a large enterprise who want to fully adopt the entire Informatica ecosystem of products for all their data operations.
6. Datameer: The non-coding data analysts who would like to improve their analytic workloads in Snowflake.
7. Hevo Data: The business expert who would like to create data pipelines and transformations via an intuitive drag-and-drop interface.

Want to dig into the details to make your decision better? Here is the full breakdown of the best data transformation tools.

The 7 best data transformation tools

1. Keboola

Keboola is a data platform as a service that helps you automate all your data operations. 

Keboola offers multiple tools to streamline transformations, but it also automates data operations surrounding ETL, ELT, and Reverse ETL data pipelines.

Every citizen can use Keboola to turn raw data into business opportunities :

1. Business users can tap into no-code transformations and build their own data pipelines with drag-and-drop functionalities (check Visual Flow Builder).
2. Engineers and data scientists can use low-code transformations in SQL, Python, R, and Julia. Alongside you get monitoring, versioning, data access control, sharing of transformations, and scalable backends with parallelization for speeding up jobs.
3. Organizations with dbt can incorporate dbt transformations into their entire workflows. And use Keboola to also run extractions and loads in ELT, ETL, and reverse ETL data flows.

Pros:

• Self-service for everyone. The options of using no-code, low-code, or dbt transformations empower every user to transform their own data in the mode that suits them best.
• Extensive coverage of transformation operations. The low-code and dbt transformations cover any imaginable data manipulation. The no-code transformations cover the vast majority of use cases with clickable pre-built solutions: filtering, data removal, data standardization, recoding values, building (aggregate) metrics and dimensions, …
• End-to-end automation. You can automate transformations by setting them on a schedule or specifying a trigger to run the transformation job.
• Fully monitored and auditable. Each transformation run is monitored for execution and can trigger alerts notifying you of data quality issues or failures. You can inspect the transformation logs to audit who touched each transformation and verify through versioning how the transformation has changed.
• Sharable and reusable. Transformations can be shared, alongside the incoming and outgoing datasets. Reuse of transformations can speed up your development time and align your team to use the same procedures.
• Scalability by design. You can adjust the resources allocated to your jobs by scaling the backend running the transformation. 
• Features beyond transformations. Keboola does not offer features and tools just for the ELT and ETL processes. The solution-rich platform helps you streamline data operations across machine learning, data management, security, sharing and documenting data via the Data catalog, etc.

Cons:

• Transformation jobs that are running too long are automatically interrupted to prevent errors or high resource consumption within non-finishing jobs. To run a fully online and continuous transformation engine with no limits, you’ll have to tweak the default transformation backend. 

Best for: teams of data scientists, data engineers, data analysts, and/or data-driven business experts who would like to transform the data from a raw mess to a goldmine of opportunities with their favorite tools (no-code, low-code, or dbt). 

2. dbt

dbt is a popular open-source transformation engine that runs SQL transformations in your data platform (data warehouse, lake, database, or query engine). It offers multiple features to streamline data operations surrounding transformations, such as scheduling, versioning, organizing, CI/CD, etc.

Pros:

• dbt transformations are highly intuitive because they are written in SQL.
• Transformations are streamlined by dbt, so you avoid the usual boilerplate and DDL code when writing SQL transformations. 
• Because transformations are executed by your data warehouse, they run in near real-time.
• Excellent support for organizing individual transformations (called “models”) into projects and documenting transformations.
• The operational features (CI/CD, versioning, collaboration) surrounding transformations are streamlined and robust.

Cons:

• Not geared toward non-technical users. You need to know SQL.
• dbt is centered on transformations only. To run a full data operation you’ll need additional tools for extracting data from its sources, loading or sending data to different destinations, managing data, controlling access, etc.
• The data warehousing adapters that allow you to run dbt within your own warehouse are limited. Sure, dbt covers the main suspects (Snowflake, BigQuery, Databricks, AWS Redshift, Postgres), but several data lakes, relational databases, and data warehouses are missing from the list.

Best for: the analytics engineer who likes to use developer best practices in SQL-driven transformations within their data warehouse. 

3. Matillion

Matillion is an open-source ETL tool that can build data pipelines through a simple no-code/low-code drag-and-drop user interface. 

Matillion creates transformation by dragging different components into a data flow. For example, you first use a “Filter” component to remove rows and then apply the “SQL” component to create a new metric.

Pros:

• The drag-and-drop user interface is user-friendly and intuitive even for non-technical users.Single data transformations scale 
• well with change data capture and batch processing ingrained in data operations. 
• Matillion can help you cover other data operations, not just transformations. It offers full support for ETL, ELT, and reverse ETL. The number and types of pre-built connectors covered by Matillion are extensive enough to cover the vast majority of data analytics use cases.

Cons:

• The no-code ETL features are unlocked at the higher tier pricing and are not part of the free offering.
• Code-based transformations are limited to SQL (no Python, R, or Julia for data scientists).
• Matillion sometimes has issues with scaling hardware infrastructure, especially EC2 instances for transformations that are more resource-hungry.
• Pricing is compounded - you pay for Matillion and the compute resources it uses to perform data operations on your cloud.
• Users often report documentation can be stale, new versions of Matillion are rarely backward compatible (so you need to do a lot of maintenance when updating the software), and there is poor support for versioning (git).

Best for: IT users at small- to medium-sized organizations who would like a simple but powerful tool to run their ETL processes.

4. Trifacta

Trifacta’s Designer Cloud is a cloud platform that integrates with your own cloud providers (AWS, Azure, Google Cloud Platform, Databricks) and runs low-code or no-code ELT or ETL pipelines.

Within the Designer Cloud, transformations are performed similarly to how you’d do them in Excel. You write the desired transformation in Trifacta’s “Wrangle Language”, an Excel-like language for data wrangling. For example, you could write “pivot by X column”. Then the Trifacta Wrangler would execute your command on the Excel-like table and pivot the table around.

Pros:

• Scales well with large data sets.
• Inferred transformations via examples. Users provide an example of a wanted outcome and Trifacta automatically infers the rule behind the transformation. 
• Automated data quality checks and data profiling by analyzing the structure of your metadata and datasets for completeness, accuracy, and validity. 
• Trifacta speeds up job execution with smart sampling algorithms. 

Cons:

• The transformations require a lot of busy work and are not user-friendly or always intuitive.
• There is no freemium model, the cheapest (and rather feature-limited) tier is $80/month. 

Best for: Enterprise users who’d like to transform their data with a solution similar to (but more powerful than) Excel.

5. Informatica

Informatica offers two main products that are focused on ETL (extract, transform, load, or the classic data pipeline) for enterprise data users:

1. Informatica PowerCenter, an ETL platform for large enterprises, and 
2. Informatica Cloud Data Integration (ICDI), a more affordable Integration Platform as a Service (IPaaS).

Transformations are made available to users in the two tools when you install Informatica Developer (the Developer tool).

Once the Transformation Engine and architecture are configured, you build transformations in a clickable user interface.

Pros:

• A highly polished and resilient product that scales seamlessly with big data needs.
• Handles numerous types of data formats commonly found in large enterprises, such as complex XML files, industry-specific enterprise data formats (SWIFT, HL7, …), and legacy data formats like COBOL copybooks.
• Informatica offers pre-built libraries of most transformations used in specific industry verticals (healthcare, finance, banking, …).

Cons:

• High vendor lock-in. To use the tool, you need to adapt your data infrastructure and architecture to the solution’s design, as well as learn their proprietary modeling language. For example, data storage options for your transformed data are very limited with Informatica. You can only use Amazon Redshift for data warehousing or Microsoft Azure SQL Data Lake as your data lake.
• Non-transparent pricing and no self-service business model. You’ll have to talk to sales and go through contracting just to demo the product. The basic Data Integration Cloud service starts at $2000 per month.
• The tool is powerful but complex. Prepare your data teams (plural) to withstand a steep learning curve before unlocking the platform’s full potential.
• Transformations are heavily built around XML data formats. Whatever your incoming data stream format, you’ll have to transform it to XML first before it can be manipulated.

Best for: A team of data engineers at a large enterprise who will invest and specialize in Informatica’s custom platform to reap the fruits of a powerful machine.

6. Datameer

Datameer helps you get the most out of your Snowflake data warehouse. It extends Snowflake’s transformation abilities by offering SQL and no-code transformations (as a drag-and-drop component) so both technical and non-technical coworkers can self-service their own data needs.

Pros:

• A better transformation tool for running Snowflake transformations. 
• The tool is heavily documented and offers many video run-throughs for common analytic use cases.
• Complex and multi-stage transformations become more intuitive by looking at their visual charts showcasing transformation dependency. 

Cons:

• Datameer is a newcomer to the market, and although it shows promise, the out-of-the-box features for no-code transformations can be lacking and some transformations are harder to execute within their libraries.
• Datameer is limited to Snowflake only.

Best for: the non-coding data analysts who would like to improve their analytic workloads in Snowflake.

7. Hevo Data

Hevo Data is a no-code data integration platform that simplifies ETL, ELT, and reverse ETL data pipelines.

Data transformations are built either by drag-and-dropping pre-built transformation blocks in the Hevo Data dashboard or by writing Python scripts to perform transformations. 

Pros:

• Covers both no-code (for business experts who don’t code) and fully-coded Python transformations.
• Alerts for failed workflows.
• Great for ETL processes involving replication - leverage CDC to speed up data copying. 

Cons:

• There are no SQL-based transformations (only Python) for writing fully customized transformations.
• Python transformations are usually limited to a simplistic function call. Complex transformations are harder to write and maintain.
• As an ETL/ELT tool, it offers limited pre-built connectors for external data sources.

Best for: The business expert who would like to create data pipelines and transformations via an intuitive drag-and-drop interface.

So out of the 7 best tools for data transformations - how do you make the final choice?

How to choose the right data transformation tool for your organization?

Keep these 3 criteria in mind when picking your preferred data transformation tool:

1. Target audience. No-code tools are best for empowering business users who don’t know how to code. Low-code tools streamline the work of the engineering teams. Pick a tool tailored to either of those two audiences (or both, if you want to empower your entire organization).

2. Pricing. Open-source solutions are usually cheaper than data platforms and tools offered by vendors (no fees, no licenses, no throttling at “unfair” consumption thresholds, and other vendor tricks). But the Total Cost of Ownership is usually greater for open-source solutions (maintenance, debugging, running your own DataOps to provision servers and instances needed for the open-source tool, etc.). Keep in mind all the possible expenses and hidden fees when evaluating your tool of choice.

3. Support and documentation. When things go wrong, is there a strong support system, such as vendor-guaranteed SLAs for support? Or if the tool is open-source, is there a strong community of users who can answer your questions? Is there extensive documentation you can rely on?

Pro tips for the data engineer

When comparing tools, performance is important. Check how the transformation tool:

1. Scales with workload resources and incoming data speeds and volumes.
2. Speeds up the transformation via parallelization.
3. Offers complementary features, such as transformation versioning, log inspections, monitoring, and alerts.

Pick Keboola - it’s best for business users and engineers

Keboola is the data platform that helps every user run the transformations they need:

1. Business users can tap into no-code transformations and build their own data pipelines with drag-and-drop functionalities (check Visual Flow Builder).
2. Engineers and data scientists can use low-code transformations in SQL, Python, R, and Julia. Alongside you get monitoring, versioning, data access control, sharing of transformations, and scalable backends with parallelization for speeding up jobs.
3. Organizations with dbt can incorporate dbt transformations into their entire workflows. 

Another thing that everyone loves is Keboola’s transparent pricing model based on usage. This means you can run your transformations (and data processes) and never fear the monthly invoice. 

And if you need any help, you can count on the award-winning support team.

Keboola is a tool that scales, streamlines, and speeds up your data transformations, so you can spend more time gaining revenue-generating insights from data.

Kebool has an always free account, no credit card required. 

Try Keboola today.

Sources:

• dbt: https://www.getdbt.com/product/what-is-dbt/
• Matillion: https://documentation.matillion.com/docs/2694747
• Trifacta: https://docs.trifacta.com/display/SS/Transform+Basics
  
• Informatica: https://docs.informatica.com/data-integration/data-services/10-2/web-services-guide/generating-web-service-soap-messages/transformation-user-interface.html
• Datameer: https://www.g2.com/products/datameer/reviews
• Hevo: https://docs.hevodata.com/pipelines/transformations/