15 Best Data Transformation Tools in 2025 (Our Top Picks)

If you work with messy data, you know the pain.

Spreadsheets grow out of control. Workflows break when someone renames a column. And trying to automate anything turns into a time-sink or a technical project you don’t have time for.

That’s where data transformation tools come in. These platforms help you clean, reshape, and prepare your data so it’s actually usable. No more fixing the same issues over and over.

At Mammoth, we’ve worked with dozens of teams making the switch. Most start with Excel or Power Query, and maybe some basic scripts. But eventually, the process slows them down. This guide will help you understand what tools are available, how they compare, and how to pick the right one based on how your team works.

Let’s dive in.

What to Look for in a Data Transformation Tool

Before jumping into specific tools, it helps to know what actually matters.

Most teams don’t need something packed with advanced features. They just need something that works reliably, saves time, and doesn’t require a steep learning curve.

Here’s what we think matters most when choosing a data transformation tool:

• Ease of use: Can someone on your team get up and running without needing to be technical? The best tools make common tasks simple.
• Repeatability: Can you run the same process again next week or next month without rebuilding it from scratch?
• Speed: How quickly can you go from messy input to clean, usable data?
• Scalability: Will the tool still work when your data grows or your team starts handling more projects at once?
• Integrations: Does it connect with the places your data already lives, like Google Sheets, databases, or cloud apps?
• Transparency: Can you see what’s happening at each step and easily trace back any mistakes?

We’ll use these criteria to compare the top tools in the next section.

Top 15 Data Transformation Tools Compared

There are plenty of tools out there claiming to help with data transformation.

Some are powerful but overkill for most teams. Others are simple but fall short once your needs grow. Below, we’ve broken down the most common tools we see teams using, and how they compare based on what actually matters.

1. Mammoth Analytics

Mammoth is built for teams who want to clean and prep data quickly, without needing engineers or complex setups. It’s fast, visual, and repeatable. So you can focus on the work, not the tooling.

What it’s best for:

• Teams outgrowing spreadsheets
• Replacing fragile workflows with repeatable ones
• Automating without code

Strengths:

• Easy to use, no setup needed
• Fully repeatable workflows
• Works across spreadsheets, databases, and APIs
• Great for collaboration and audit trails

Things to consider:

• Not designed for heavy-duty data science
• Built more for speed than deep customization

2. Alteryx

Alteryx is one of the biggest names in data prep and transformation. It’s powerful and flexible, but can feel heavy for smaller teams or quick-turnaround work.

What it’s best for:

• Enterprise teams with big budgets
• Complex workflows and deep integrations

Strengths:

• Advanced features and customization
• Strong support and documentation
• Handles large data volumes well

Things to consider:

• Expensive
• Steep learning curve for new users
• Requires more setup and maintenance

Interested in learning more about Alteryx? Read our full breakdown on Alteryx’s pricing.

3. Dataiku

Dataiku is a platform aimed at data science teams. It includes data prep, machine learning, and more, all in one place. Like Alteryx, it’s powerful but might be more than what most teams need.

What it’s best for:

• Technical teams doing machine learning and analytics
• Enterprise environments with long-term projects

Strengths:

• Supports code and no-code users
• Wide range of features
• Scales well across big teams

Things to consider:

• Requires technical knowledge to get full value
• Slower to onboard
• Higher cost and complexity

Interested in learning more about Dataiku? Read our full breakdown on Dataiku’s pricing.

4. Excel / Power Query

Still the go-to tool for many teams, Excel is everywhere. And with Power Query, it’s gotten better at handling simple transformations. But it doesn’t scale well, and processes break easily.

What it’s best for:

• One-off tasks
• Simple data cleanup
• Individual users

Strengths:

• Familiar interface
• Low barrier to entry
• Fine for small, manual jobs

Things to consider:

• Hard to repeat or scale
• Easy to make errors that go unnoticed
• Not built for collaboration

5. Talend

Talend is an open-source tool aimed more at developers. It’s powerful, but the technical barrier means it’s not the right fit for most non-engineering teams.

What it’s best for:

• Developer-heavy teams
• Complex data integrations and pipelines

Strengths:

• Flexible and customizable
• Free version available
• Strong community

Things to consider:

• Steep learning curve
• Requires technical skill
• Slower for simple use cases

6. Trifacta (Now part of Alteryx)

Trifacta was known for its smart, visual approach to data cleaning. It’s now part of Alteryx, which makes it a good fit for teams already considering using it.

What it’s best for:

• Data cleanup within Alteryx workflows
• Technical teams looking to automate prep

Strengths:

• Strong AI suggestions
• Good for structured datasets
• Fits into larger Alteryx data stack

Things to consider:

• Tied to Alteryx
• Less intuitive for non-technical users
• Slower for simple jobs

7. Apache NiFi

Apache NiFi is open-source and built for automating data flows between systems. It’s used by data engineers, not analysts, and can handle high-volume tasks.

What it’s best for:

• Moving and processing large streams of data
• Complex enterprise environments

Strengths:

• Great for real-time pipelines
• Supports a wide range of data formats
• Open-source and flexible

Things to consider:

• Very technical
• Not built for analysis or business users
• Steep setup curve

8. Knime

Knime is an open-source platform with strong visual programming features. It’s used for analytics, machine learning, and data prep, but can get complicated fast.

What it’s best for:

• Analysts and data scientists
• Research teams doing modeling and prep

Strengths:

• Visual workflow builder
• Big community and library of extensions
• Free to use

Things to consider:

• Interface feels outdated
• Can get complex quickly
• Requires some technical know-how

9. Microsoft Power BI (for prep)

Power BI includes some data prep functionality, especially through Power Query. It’s a good option for teams already committed to Microsoft tools.

What it’s best for:

• Basic transformation alongside reporting
• Microsoft-heavy organizations

Strengths:

• Integrated with Excel and Office 365
• Easy to share dashboards
• Good for small teams

Things to consider:

• Limited transformation power
• Workflows can break easily
• Not ideal for automation

Struggling to easily use Power BI? Check out our top Power BI templates.

10. Rivery

Rivery is a cloud-native platform that focuses on automating data workflows and transformation. It’s aimed at mid-size teams and SaaS companies.

What it’s best for:

• ETL and ELT for SaaS companies
• Automating recurring workflows

Strengths:

• Low-code interface
• Prebuilt connectors
• Flexible scheduling

Things to consider:

• More expensive than simpler tools
• Requires some setup and learning
• Not as strong for exploratory analysis

11. Hevo Data

Hevo is a no-code platform for data pipeline management and transformation. It’s designed to be simple and quick to use, with lots of connectors.

What it’s best for:

• SaaS companies looking for automated syncing and cleanup
• Teams without dedicated engineers

Strengths:

• No-code UI
• Real-time data sync
• Easy onboarding

Things to consider:

• More focused on movement than deep transformation
• Less control over complex logic

12. Matillion

Matillion is a cloud-based ETL tool built for platforms like Snowflake, Redshift, and BigQuery. It’s strong for transformation but leans technical.

What it’s best for:

• Cloud-native data teams with large volumes
• Teams doing advanced transformations in the warehouse

Strengths:

• Deep cloud integrations
• Visual interface for SQL workflows
• Strong performance

Things to consider:

• Requires technical knowledge
• Geared more toward ETL than light data prep

13. Keboola

Keboola is a platform that combines data prep, orchestration, and analytics. It’s flexible but mostly suited to data-savvy teams.

What it’s best for:

• Mid-size data teams managing multiple pipelines
• Data integration across tools

Strengths:

• Modular and customizable
• Cloud-native
• Supports full data lifecycle

Things to consider:

• Can be overkill for small teams
• Learning curve to get value

14. Coefficient

Coefficient plugs directly into Google Sheets and adds live data syncing, transformation tools, and automations. It’s ideal for spreadsheet-first teams.

What it’s best for:

• Google Sheets users wanting more power
• Business teams who don’t want to leave spreadsheets

Strengths:

• Very fast to set up
• No learning curve
• Makes Sheets much more powerful

Things to consider:

• Still limited by the structure of a spreadsheet
• Not built for large datasets or heavy workflows

15. Parabola

Parabola is a drag-and-drop platform focused on automating workflows, especially for operations and ecommerce teams.

What it’s best for:

• Operations teams without engineers
• Repetitive workflows across tools

Strengths:

• Visual and easy to learn
• Strong for repetitive tasks
• Flexible logic builder

Things to consider:

• Less suited for large or technical data sets
• Limited advanced transformation features

Great — here’s the Comparison Table section in a simple, scannable format. Since we’re publishing this on the web, this version is written to be easy to convert into an actual HTML table, grid, or styled component in your CMS.

Data Transformation Tools Comparison Table

Try a Simpler Way to Transform Your Data

Most teams don’t need an enterprise platform with hundreds of features. They need something that works, that’s easy to repeat, and that doesn’t require a full-time engineer to maintain.

That’s exactly what we built Mammoth for.

You can:

• Connect to your data in minutes
• Build repeatable workflows without writing code
• Clean, combine, and structure data for reporting or automation
• Share results with your team instantly

If your current setup is slowing you down, give Mammoth a try.

You can start free or book a demo to see how it works.

👉 Book a demo

FAQs

What is a data transformation tool?

A data transformation tool helps you clean, reshape, and organize data so it’s ready for reporting, analysis, or automation. It saves time and reduces errors by letting you build workflows instead of doing everything manually.

Do I need to know how to code to use these tools?

It depends on the tool. Some, like Mammoth, are designed for non-technical users and require no coding. Others, like Talend or Apache NiFi, are built for engineers and require technical skills.

What’s the difference between data transformation and data integration?

Data integration is about pulling data together from different sources. Transformation is what you do after that — cleaning it, combining it, and structuring it so it’s usable.

Can I use Mammoth alongside my current tools?

Yes. Mammoth works with spreadsheets, databases, and other platforms, so you don’t need to change your entire stack to start seeing value.