The 6 Best FREE Open-Source ETL Tools in 2023

How to choose your free and open-source ETL tool?

When you’re looking for an ETL solution to automate your data pipelines you need to keep in mind the following:

1. Connector coverage. Check the tool covers all your different data sources and destinations. It would be a shame if it only covers Google BigQuery with a source data connector, but you operate within the Windows ecosystem of tools. If a connector is not pre-built, check the tool is extensible and can cover new data sources and complex data parsing, like processing unstructured data. 
2. Target audience. Who is the tool built for? Some tools are for developers, and you need to check whether you know the tool’s language (Java-based tools need a different skill set than SQL-based tools) and use cases (can you build API workflows via CLI scripting?). Maybe you need a data integration tool that can service business experts with no scripting knowledge. In this case, look for a no-code graphical drag-and-drop user interface.
3. Ease of use. Can the tool be configured and run in a couple of minutes, or is it fueled by sweat?
4. Ease of customizability. Not all open-source ETL tools offer the same level of customizability. Especially in the extractor features. Maybe you need batch processing or filtering at extraction to avoid heavy data loads. Check if the tool can be flexibly adjusted to your needs.
5. Data transformation capabilities. Open-source ETL tools most notably vary in how they perform transformations. Some (usually the no-code ones) offer only canned transformations. Others offer a full set of flexible transformations via scripting in programming languages (Python, SQL, Java, …).
6. Scalability. Can the tool grow with your data needs? If a tool offers big data features, such as processing data flows with high-performance Hadoop clusters, you can be fairly sure the tool can grow with your data loads. 
7. Security. Open-source ETL tools are usually worse than vendor tools at security. From regulation-compliant data processing (GDPR) to lineage tracing, check how your tool keeps your data safe at rest and in transit. If you operate in an especially sensitive area, pick tools that can be run on-premise where you can configure your own complex data security instead of web-based or cloud-based tools.
8. Support. From actual phone calls to documentation and Stack Overflow coverage - what levels of support will you get when (not if) things go wrong?
9. The total cost of ownership. Open-source ETL tools are renowned for their low entry costs. There are no vendor fees, no licensing, and no consumption caps. 

The best ETL tools will check all 9 criteria. Let’s check which are the best ones on the market.

The 6 best ETL tools that are free and open-source

1. Keboola

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

Its core feature is to build and automate ETL, ELT, and reverse ETL data pipelines.

Keboola operates at the intersection of open-source and freemium vendor technology. You can use its open-sourced components to extract and load data from multiple endpoints. With the dbt integration, you can use open-source and production-quality transformations.

The freemium aspect means you get to access enterprise-grade automation features at a fraction of the price. From security to DevOps, the platform takes care of all your data needs. All these features are free of charge within the freemium model.

Pros:

• One of the largest libraries of pre-built components. Keboola components are pre-built modules that help you extract and load data between multiple endpoints:

• Relational databases (MySQL, Oracle, Postgres …) 
• SaaS apps (Salesforce, CRMs, Facebook Ads, …), 
• files (JSON, Excel, XML, CSV), 
• cloud data warehouses (BigQuery, Snowflake, AWS Redshift, Microsoft Azure) 
• … and many others (check the full list of 250+ pre-built connectors here).

• Fully extensible. If there is no pre-built connector, you can use the Generic Extractor that can collect data from any API-like source or the Generic Writer to load data to any source. 
• No-code and low-code transformations. In Keboola, you can use a fully flexible scripting transformation in Python, SQL, R, or Julia, use dbt transformations, or use pre-built no-code transformations. 
• End-to-end automation. Data flows can be automated with Orchestrators and Webhooks. Every job is fully monitored, so you can always keep an eye on execution. And every ETL data pipeline can easily be shared and reused to save you development time. 
• Easy to use. Keboola is all about democratization. It offers developer tools (CLI, CI/CD, git, IDEs, …) and no-code tools (visual builders, drag-and-drop graphical dashboards) to make the life of your users easier.
• Extensive data use cases beyond ETL: enterprise data security for every business size, data governance and master data management, DataOps (development branches and versioning, CDC to speed up replication, CLI), Data Catalog for data sharing, and machine learning toolbox.

Cons:

• Keboola is not great for real-time data flows. Keboola offers near real-time data integration. Orchestrators can trigger data extraction every 1 minute and webhooks can be used for almost instantaneous data collection from different sources. But Keboola is not a data streaming service and does not offer continuous data extraction. 

Best for: Teams of technical data experts (scientists, engineers, analysts) and data-driven business experts who would like an all-in-one ETL solution.

“Keboola puts you in a full control of your data. We have a lot of options to choose from in one platform. It gives us enough room for creativity in approaches to data transformation. It helps us to consume the data and insights in the most suitable way for us.” Monika S., Head of data team

“Instead of separately selecting, acquiring, configuring and integrating endless list of technologies to build your data stack, Keboola gets you there in one platform.” Robert C., Head of Product at Gymbeam 

2. Talend Open studio

Talend Open Studio is an open-source data integration platform that enables you to execute ETL tasks and cloud or on-premise jobs using Spark, Hadoop, and NoSQL databases. 

Talend Open Studio is a product of Talend which also offers paid data integration software, such as Talend Data Fabric as a managed data service for developers, Stitch as a no-code data ingestion tool geared towards analysts, or add-on services like Talend Data Quality and Talend Profiling. But we’ll focus on its popular open-source offering.

Pros:

• Can build scalable ETL and ELT data pipelines.
• Simple GUI interface that helps you visualize data pipelines.
• Over 1000 connectors help you integrate business and data endpoints.
• Capable of simple and complex transformations.

Cons:

• Documentation for features is often lacking.
• Many components are reserved for the paid tear (check which ones are available as open source here).
• RAM hungry - not optimized for transformations and certain components tend to cause bottlenecks.
• A lot of big data features are locked behind the paywalls of its paid services.
• Writing transformations is labor-intensive.

Best for: The savvy data engineer who likes to tinker with code (solo data member or weekend hobbyist). The ideal user is willing to trade more coding time for a less polished (more high-maintenance) solution that will save money on licensing and usage costs.

3. Pentaho data integration (previously, Kettle)

Pentaho Data Integration (PDI) is an open-source data integration tool that focuses on Extract, Transform and Load (ETL) capabilities to facilitate data engineering work. 

Previously, it was known as Kettle, but after the Hitachi Vantara acquisition, the open-source data integration platform was renamed. It’s a metadata-driven ETL tool that helps you integrate data across different storages and repositories.

Pros:

• Strong DBA offerings: database replication, data migration, supports slowly changing dimensions and schemas in data warehousing, etc.
• Canned transformations - Pentaho comes with samples that show you how to transform data. The canned transformations are customizable, and PDI offers strong support for complex transformation jobs.
• The visual representation of the ETL workflow helps you understand complex processes.
• Can parallelize data processing and use multithreading for removing data pipeline bottlenecks.
• Low-code - the tool is geared toward a technical audience but implemented as a clickable (not extensively codable) solution.

Cons:

• Click-intense: it takes a lot of steps to build a simple pipeline. 
• Missing a lot of business connectors, such as any SaaS apps. It is mostly designed for database-to-database data pipelines.

Best for: Cost-sensitive database administrators who want to streamline their jobs with an open-source tool.

4. Singer

Singer is an open-source standard for ETL software. A lot of open-source and commercial vendors use Singer as the barebone for their own ETL offerings.

Singer is built around data extraction scripts called “taps” and data loading scripts called “targets”. 

Pros:

• Supports a wide range of DBA (databases, data lakes, data warehouses) and SaaS apps commonly found in startups.
• JSON-based data flow configuration, which makes it transparent and flexible.
• Supports incremental extraction for big data loads.

Cons:

• Little to no support for transformations. It is geared towards data integration, aka moving data from one place to another, with a bit of filtering in between, but no serious transformation features are available. 

Best for: The data engineer covering data integration in a startup environment.

5. Airbyte

Airbyte is an ETL platform for data integration. You can buy the vendor cloud-based solution (14-days free trial) or run it as an open-source self-deployed solution.

In Airbyte, you build your ETL data pipelines by linking the correct component blocks via a graphical dashboard that is intuitive and user-friendly.

Pros:

• You can build new connectors for a custom source or destination using the Airbyte Connector Development Kit within an hour or modify an existing connector to suit your use cases.
• Real-time alerts with webhooks.
• Integrates with Kubernetes, Airflow, or dbt for orchestrations and transformations.

Cons:

• No custom transformation tools, you will have to link or purchase another tool for the transformation layer. This can incur additional data transferring costs.
• The normalization features sometimes fail, producing hubris tables in the data warehouse.

Best for: Data engineer at a startup that has many straightforward BI or app data analytics use cases.

6. Apache NiFi

Apache NiFi is a web-based data flow builder. You construct data pipelines via a graphical user interface that builds workflows as DAGs (directed acyclic graphs).

Pros:

• Highly-configurable workflows - from throughput to latency control, you can really customize Apache NiFi.
• It can be used for real-time data streaming.
• The Apache NiFi’s data provenance module allows you to track data throughout its lineage.

Cons:

• Despite offering some security features (HTTPS for data in transit, authorization, …) Apache NiFi does not offer extensive security features out of the box.
• The configuration and troubleshooting are highly-technical. The target user needs deep knowledge of Java and telecommunication protocols in IT to debug system failures.

Best for: SysAdmins and DevOps professionals building data flows.

Set up ETL processes that scale in a couple of minutes with Keboola

There are a lot of free and open-source tools on the market. But Keboola offers the best value for money.

The freemium model unlocks all the open-source ETL, ELT, and reverse ETL features, without even swiping the credit card.

And you don’t have to worry about deployment or maintenance issues. Keboola takes care of all the heavy lifting in the background.

Try Keboola for free today. 

Build your first ETL data pipelines in minutes.

Image sources:

• Talend Open Studio: https://www.g2.com/products/talend-cloud-data-integration/reviews
• Pentaho Kettle: https://www.g2.com/products/pentaho-business-analytics/reviews
• Singer: https://medium.com/@ash_hathaway/supercharging-your-etl-with-airflow-and-singer-3543ddb3c185
• Airbyte: https://www.g2.com/products/airbyte/reviews
• Apache NiFi: https://nifi.apache.org/