Data transformation tools help change data formats, apply business logic, and otherwise play the "T" role in ETL. This is especially important when consolidating both structured and unstructured data from disparate sources for analysis. These tools also allow you to add more information to the data before "loading" it for analysis.
A good data transformation tool plays a critical role in the whole data integration process and makes the job much easier.
Getting this part wrong can quickly lead to data loss, incompatibility issues, and a loss of valuable insights. Because of this, it's important to choose the right data transformation tool.
But how do you know what's right for you?
At Panoply we are passionate about everything data. So, to make your life (a little bit) easier, we dove deep and compared various different data transformation tools.
We looked at things like usability, features, price, and more to come up with a list of the 8 best data transformation tools you should consider.
1. dbt
If you already know SQL, dbt might be the right tool for you. You can take control of the whole data pipeline and write code to initiate data transformation quickly.
It natively supports Git integrations, logging, modularity, and version control.
As every data model is a simple SELECT statement, it's easy to turn SQL queries into tables and views in major data warehouses like BigQuery, Redshift, and Snowflake.
What's cool about dbt is the "ref function," which allows you to reference one data model within another.
This approach also helps automatically generate dependency graphs. So, when you update the data, all your materialized tables in the graph are also updated in the correct order.
TL;DR: dbt makes data transformation easy for analytics engineers, but it can prove to be a challenge for less techy colleagues on your team.
dbt pricing: dbt Core is free and open-source for one data engineer, and dbt Cloud (which is best suited for larger data teams) starts at $50 per month per person.
2. Airflow
Apache Airflow is the whole package.
Once you build your codebase, you can quickly run and manage the entire extraction, transformation, and enrichment process.
You can also conduct quality checks and swap out the extraction and transformation code as needed.
Beyond data transformation, you can also use Airflow to organize, monitor, and schedule complex data workflows as directed acyclic graphs (DAG), a topological representation of data flows within the system.
Using Airflow, developers can quickly transform data as an action within a workflow by writing Python code. You can also manage execution dependencies among jobs in the DAG and effectively manage job failures, alerts, and retires programmatically.
TL;DR: Airflow is the Audi R8 of data transformation tools that make handling large complex data flows easy. However, those who benefit from its flexibility are mostly Python ETL tool users.
Airflow pricing: free and open-source.
3. EasyMorph
EasyMorph is an excellent data transformation (with the E and L) tool for less technical business users and analytics departments. It helps you automate complex data transformations and repetitive tasks without writing a single line of code.
This tool comes with over 100 actions and 120 functions out of the box, so even users without a technical background can use this powerful Windows-based visual tool to build advanced workflows with conditional branching, loops, and parameters.
TL;DR: EasyMorph gets non-techy users transforming data within minutes. However, users report only extensive support for data sources when it comes to inputs but not outputs. Their documentation is also sometimes outdated.
EasyMorph pricing: $750 per year or $250 for three months. A free version is also available.
4. Dataform
Like dbt, Dataform is a free and open-source data transformation tool that lets you manage all your processes in cloud data warehouses (including Panoply, Snowflake, Redshift, and BigQuery).
If you're familiar with SQL, you can quickly leverage SQLX to build robust data transformation pipelines using the command line.
Similar to dbt, each SQLX model is like a simple SELECT statement. This means data engineers can quickly create dependencies between tables with the ref function within a few minutes and spend more time engaging in analytics than managing data infrastructure.
If you don't want to code, you'll be happy with their paid tier called Dataform Web. It offers a browser-based IDE that's highly intuitive and allows you to schedule complex tables, dependencies, and views. You can also seamlessly manage pipelines, testing, and documentation, and share data models across your team.
TL;DR: Dataform is a powerful data transformation tool for your SQL-loving data engineers. While it's more user-friendly than dbt, it's supported by a smaller community and only used by a few companies.
Dataform pricing: Dataform Core is free for life, but there's a waitlist. Dataform Web starts at $150 per month for 5 users.
5. Matillion
Matillion enables data transformation by defining functions in SQL or through its simple point-and-click interface.
Designed to integrate data with Azure Synapse, BigQuery, Redshift, and Snowflake, Matillion enables seamless consolidation of large data sets (including raw data) for quick, on-demand, transformations.
This tool takes the work out of ETL processes and is equipped with over 70 pre-built connectors.
What's really useful is that Matillion also offers expert technical support provided by solution architects, completely free.
TL;DR: Matillion is ideal for enterprises and mid-market companies that host data teams with a mixed set of skills. However, their pricing model is quite complex and sometimes out of reach of light users.
Matillion pricing: Matillion offers flexible pricing plans starting at $2 per credit, but a free version and trial are also available.
6. RudderStack
RudderStack is a smart customer data pipeline that lets users transform any data type.
The RudderStack Transformations API is a popular feature that allows users to programmatically add and remove transformations, define libraries, version control transformation, and share them within a sandbox with your team before going live.
Once the data is collected, you can transform, debug, manage, and run it in real-time before delivering it to its final destination.
As it's a RESTful API, you can build and use the transformation and receive a JSON object response. To determine the success, or failure, of your API requests, you can use standard HTTP response codes.
TL;DR: RudderStack is an innovative tool that allows experienced users to transform data effortlessly. However, less technical users might find it overwhelming to get over the learning curve.
RudderStack pricing: starts at $750 for 25 million events per month, and a free version (for 500,000 events per month) is available.
7. Trifacta
Trifacta is an intuitive data wrangling and visual data engineering cloud platform that helps data teams prepare, cleanse, transform, and visualize unstructured data. You can also ensure quality, automate data pipelines, and generate reports.
It's the perfect data transformation tool that enables data analysts, engineers, and scientists to collaborate, streamline operations, and get them ready for advanced analytics.
Trifacta is also quite flexible and allows users to connect to any data source quickly. By leveraging its AI-powered self-service approach, data teams can easily evaluate, validate, and accelerate data transformations.
However, some users complain that it's pretty buggy and that it lacks statistical modules for analysis and predictive modeling.
TL;DR: Trifacta is a smart data wrangling and transformation solution developed for experienced data professionals. However, you'll have to work around the bugs in the system.
Trifacta pricing: starts at $80 per user per month (plus $0.60 per vCPU hour), and a 30-day free trial is available.
8. pandas Python
pandas is an open-source Python package that's essentially a high-level building block for engaging in practical, real-world data analysis. Its powerful, yet flexible, group of functions enable split-apply-combine operations on data sets for both aggregation and transformation.
However, if you're working in a team with a mix of skills, it's best to connect and leverage a tool like Panoply to integrate pandas and Python into a BI tool.
This approach helps eliminate mundane setup and maintenance tasks while taking advantage of Panoply's built-in storage.
TL;DR: pandas Python is an excellent tool to engage in advanced analytics, but this doesn't mean that it's only suitable for Python ninjas. By connecting it to the right BI tool, you can also accommodate business users on your team.
pandas Python pricing: free and open-source.
Comparing data transformation tools
So, what's the best data transformation tool for your organization?
As it is when it comes to choosing a transformation tool, the answer ultimately comes down to your specific business goals and available resources. The best approach here is to find a balance between technical know-how and your budget.
At Panoply, we're fanatical about all things data. That's why we provide robust ETL pipelines and data warehousing solutions with unparalleled support.
To get a feel for what we do, get started for free or request a personalized demo.
