Top 7 Free Open-source ETL Tools to Consider in 2024 – Ranked by Popularity

As data continues to grow at an unprecedented rate, the need for an efficient and scalable open-source ETL solution becomes increasingly pressing. However, with every organisation’s varying needs and the cluttered market for ETL tools, finding and choosing the right tool can be strenuous.

I reviewed over 10 ETL Tools and have curated an open-source ETL tools list, ranked by popularity with their features, pros and cons, and customer reviews to help you choose a tool that aligns with your data requirements and supports hassle-free data integration capabilities.  

For some of you who is ever curious, our team also compiled a list of other data integration tools that you could leverage.

Comparing the Best Free Open-source ETL Tools

1. Meltano

G2 Rating: 4.9

Founded in: 2018

Initially created for GitLab’s data and analytics team, it quickly became apparent how powerful this open-source tool could be for anyone needing an end-to-end data platform. Built around open-source components and DevOps principles, Meltano is built to streamline workflows and make your projects more efficient and collaborative.

Meltano ETL Features

• Plug & Play: Meltano offers 600+ pre-built taps and targets, making the entire setup process a breeze.
• Pipeline Transparency: Meltano gives full transparency into your data workflows with detailed pipeline logs. Plus with the availability of source codes, you can modify or debug any issue in your pipeline quickly.
• Version Control: With Meltano, I could store my pipelines and configuration files in Git and roll back the last good version of the pipeline if anything goes wrong. And, your entire team can propose changes as pull requests in Git, promoting collaboration.
• Testable: You can experiment with new connectors, configurations, or even entire pipelines on your local machine first. Once you’re happy with the changes, you can verify everything in the staging environment before pushing anything live.

Pros

• Open Source: You can either use the self-host version of the tool to deploy your own infrastructure or get the open source support package, in case you need their help.
• Command line interface(CLI): Meltano is incredibly empowering for those who want a more granular control over their data transformations. I can manage my data pipelines with simple commands, which saves a lot of time compared to clicking through multiple buttons.
• Customizable Connectors: Meltano offers SDK that lets you connect to those niche data sources or internal systems that aren’t standard.
• Active Community: A user on G2 says “Meltano is reliable for the long-haul. The community (mostly on Slack) is easily one of the most friendly, responsive, active, and knowledgeable”

Cons

• While Meltano is rapidly evolving, its documentation may sometimes lag behind. However, I’ve found great support in their Slack community to address any gaps.
• Meltano’s orchestration capabilities are somewhat limited, especially for complex pipelines requiring extensive customization. Yet, for most scenarios, Meltano’s built-in features suffice, although some taps/targets may require occasional adjustments.

Meltano Resources

Documentation | Community | Blogs

2. dbt (Data Build Tool)

G2 Rating: 4.8

Founded in: 2021

dbt is an open-source software tool designed for data professionals working with massive data sets in data warehouses and other storage systems. It enables data analysts to work on data models and deploy analytics code together using top software engineering practices, such as modular design, portability, continuous integration/continuous deployment (CI/CD), and automated documentation.

dbt ETL Features

• SQL-based Transformations: I used SQL to make direct transformations to data, without relying on external transformation languages or ELT tools that use graphical interfaces. 
• Data Warehouse-Oriented: I transformed and modeled data within the data warehouse, such as Snowflake, BigQuery, or Redshift, instead of extracting, transforming, and loading (ETL) data into a separate data space.
• Built-in Testing: dbt’s built-in testing feature checks for data integrity and accuracy during transformations, helping to catch and correct errors easily and efficiently. 

Pros

• Open-source: dbt, being open-source, offers an extensive library of installation guides, reference documents and FAQs. It also offers access to dbt packages, including model libraries and macros designed to solve specific problems, providing valuable resources. 
• Auto-generated documentation: Maintaining data pipelines becomes easy for users like you as the documentation for data models and transformations is automatically generated and updated. 

Cons

• dbt can only perform transformations in an ETL process, therefore, you’ll need other data integration tools to extract and load data into your data warehouse from various data sources.
• If you’re not well-versed in SQL, it won’t be easy for you to utilize dbt as it is SQL-based. Instead, you could find another tool that provides a better GUI. 

dbt Resources

Documentation | Developer Blog | Community

3. Airbyte

G2 Rating: 4.5

Founded in: 2020

Airbyte is one of the top open-source ELT tools with 300+ pre-built connectors that seamlessly sync both structured and unstructured data sources to data warehouses and databases. 

Airbyte ETL Features

• Build your own Custom Connector: Airbyte’s no-code connector builder allowed me to create custom connectors for my specific data sources in just 10 minutes. Plus, the entire team can tap into these connectors, enhancing collaboration and efficiency.
• Open-source Python libraries: Airbyte’s PyAirbyte library packages Airbyte connectors as Python code, eliminating the need for hosted dependencies. This feature leverages Python’s ubiquity, enabling easy integration and fast prototyping. 
• Use Airbyte as per your Use case: Airbyte offers two deployment options that can fit your needs perfectly.  For simpler use cases, you can leverage their cloud service. But for more complex pipelines, you can self-host Airbyte and have complete control over the environment.

Pros

• Multiple connectors: Through its wide availability of connectors, Airbyte simplifies and facilitates data integration. Users on G2 acclaim it as ” a simple no-code solution to move data from A to B”, ” a tool to make data integration easy and quick,” and “The Ultimate Tool for Data Movement: Airbyte.”
• No-cost: As an open-source tool, Airbyte eliminated the licensing costs associated with proprietary ETL tools for me. A user on G2 claims Airbyte to be “cheaper than Fivetran, easier than Debezium”
• Handles large volumes of Data: It efficiently supports bulk transfers. A user finds this feature the best about Airbyte: “Airbyte allowed us to copy millions of rows from a SQL Server to Snowflake with no cost and very little overhead”.

Cons

• As a newer player in the ETL landscape, Airbyte does not have the same level of maturity or extensive documentation compared to more established tools.
• The self-hosted version of Airbyte lacks certain features, such as user management, that makes it less streamlined for larger teams.

Airbyte Resources

Documentation | Roadmap | Slack 

4. Apache Kafka

G2 Rating: 4.5

Founded in: 2011

Apache Kafka is one of the best open source ETL tools with a distributed platform that enables high-performance data pipelines, real-time streaming analytics, seamless data integration, and mission-critical applications through its robust event streaming capabilities, widely adopted by numerous companies.

Apache Kafka ETL Features

• Scalable: I found Kafka to be incredibly scalable, allowing me to manage production clusters of up to a thousand brokers, handle trillions of messages per day, and store petabytes of data. 
• Permanent Storage: Safely stores streams of data in a distributed, durable, and fault-tolerant cluster.
• High Availability: Kafka’s high availability features allowed me to efficiently stretch clusters across availability zones and connect separate clusters across geographic regions. 
• Built-in Stream Processing: I utilized Kafka’s built-in stream processing capabilities to process event streams with joins, aggregations, filters, transformations, and more. This feature was particularly useful for real-time data processing and analytics
• Wide Connectivity: Kafka’s Connect interface integrates with hundreds of event sources and sinks, including Postgres, JMS, Elasticsearch, AWS S3, and more.

Pros

• Handles large volumes of Data: Kafka is designed to handle high-volume data streams with low latency, making it suitable for real-time data pipelines and streaming applications. Apache Kafka users on G2 rate it as “Easy to use and integrate” and “Best option available to integrate event based/real-time tools & applications”.
• Reliability: Being open-source, Apache Kafka is highly reliable and can be customized to meet specific organizational requirements. Sarthak A. on G2 rates it as the “Best open-source processing platform”.

Cons

• Kafka lacks built-in ETL capabilities like data transformation and loading, requiring additional tools or custom development to perform these steps effectively.
• The setup and maintenance of Kafka can be complex, making it less suitable for simple ETL pipelines in small to medium-sized companies.

Apache Kafka Resources

Documentation | Books and Papers

5. Pentaho Data Integration

G2 Rating: 4.3

Founded in: 2004

Previously known as Pentaho Kettle, it is an open-source ETL solution that was acquired by Hitachi Data Systems in 2015 after its consistent success with enterprise users. Pentaho offers tools for both data integration and analytics, which allows users to easily integrate and visualize their data on a single platform. 

Pentaho ETL Features

• Friendly GUI: Pentaho offers an easy drag-and-drop graphical interface which can even be used by beginners to build robust data pipelines.
• Accelerated Data Onboarding: With Pentaho Data Integration, I could quickly connect to nearly any data source or application and build data pipelines and templates that run seamlessly from the edge to the cloud.
• Metadata Injection: Pentaho’s metadata injection is a real time saver. With just a few tweaks, I could build a data pipeline template for a common data source and reuse it for similar projects. The tool automatically captured and injected metadata, like field datatypes, optimizing the data warehousing process for us.  

Pros

• Free open-source: Pentaho is available as both a free and open-source solution for the community and as a paid license for enterprises. 
• Pipeline Efficiency: Even for users without any coding experience, you can build efficient data pipelines yourself, giving time to focus on complex transformations and turn around data requests much faster for the team. A user on G2 says “Excellent ETL UI for the non-programmer”.
• Flexibility: Pentaho is super flexible, I could connect data from anywhere: on-prem databases, cloud sources like AWS or Azure, and even from Docker containers.

Cons

• The documentation could be much better; finding examples for all the functionalities PDI offers can be quite challenging.
• The logging screen doesn’t provide detailed error explanations, making it difficult to identify the root cause of issues. Additionally, the user community isn’t as robust as those for Microsoft or Oracle.
• Unless you pay for the tool, you’re pretty much on your own for implementation.
• PDI tends to be a bit slower compared to its competitors, but other than that, I don’t have major complaints about the tool.

Pentaho Resources

Community | Documentation | Stack Overflow

6. Singer

G2 Rating: NA

Founded in: NA

Singer is an open-source standard ETL solution sponsored by Stitch, for seamless data movement across databases, web APIs, files, queues, and virtually any other imaginable source or destination. Singer describes how the data extraction scripts – “Taps” and data loading scripts – “Targets” should communicate, facilitating data movement.

Singer ETL Features

• Unix-inspired: No need for complex plugins or running daemons with Singer, it simplifies data extraction by utilizing straightforward applications connected through pipes. 
• JSON-based: Singer is super versatile and avoids lock-in to a specific language environment since it follows JSON based communication, meaning you can use any programming language you’re comfortable with.
• Incremental Power: Singer’s ability to maintain state between runs is a huge plus. This means you can efficiently update your data pipelines without grabbing everything from scratch every time. It’s a real time saver for keeping your data fresh.

Pros

• Data Redundancy and Resilience: Singer’s tap and target architecture allowed me to load data into multiple targets, significantly reducing the risk of data loss or failure. 
• Efficient Data Management: Singer’s architecture enables you to manage data more efficiently. By separating data producers (taps) from data consumers (targets), you can easily monitor and control data flow, ensuring that data is properly processed and stored.

Cons

• While the open-source nature of Singer offers flexibility in leveraging taps and targets, adapting them to fit custom requirements can be challenging due to the absence of standardization. This sometimes makes it tricky to fully utilize the connectors to meet your specific needs.

Singer Resources

Roadmap | Github | Slack

7. PipelineWise

G2 Rating: NA

Founded in: 2018

PipelineWise is an open-source project developed by TransferWise, initially created to address their specific requirements. It is a Data Pipeline Framework that harnesses the Singer.io specification to efficiently ingest and replicate data from diverse sources to a range of destinations.

PipelineWise ETL Features

• Built for ETL: Unlike traditional ETL Tools, PipelineWise is built to seamlessly integrate into the ETL workflow. Its primary purpose is to replicate your data in its original format from source to an Analytics-data-store, where complex mapping and joins are performed. 
• YAML-based configuration: I defined my data pipelines as YAML (yet another markup language) files to ensure all the configurations were under version control.
• Replication Methods: PipelineWise supports three data replication methods—log-based change data capture (CDC), key-based incremental updates, and full table snapshots.

Pros

• Lightweight: Pipelinewise is lightweight, so I didn’t have to set up any daemons or databases for operations.
• Security: PipelineWise is ideal for obfuscating, masking, and filtering sensitive business data. This ensures that such data is not replicated in your warehouse during load-time transformations. 

Cons

• While PipelineWise supports micro-batch data replication, the creation of these batches adds an extra layer to the process, causing a lag of 5 to 30 minutes, making real-time replications impossible.
• There is no community, so no support is provided for PipelineWise, but it has open-sourced documentation available. 

PipelineWise Resources

Documentation | Licenses | Slack

Last but not least, Talend Open Studio deserves a special mention as a free open-source ETL tool that has been available for the past 20 years. However, due to declining community adoption, Talend has made the difficult decision to discontinue the open source version of Talend Studio. In response, they have partnered with Qlik to offer both free and paid versions of their data integration platform, continuing their commitment to data quality and integration.

Apart from these 7 most popular free open-source ETL tools, I also tried the following 3 open-source tools that have been making a buzz in the market and are definitely worth a try.

• pygrametl: pygrametl is a free, open-source ETL tools Python library built for developers who like to get control of their pipelines. It offers tools specifically designed for building ETL (Extract, Transform, Load) pipelines. As an ETL framework, pygrametl focuses on the data processing logic within your ETL pipelines, assuming the tables already exist, rather than creating the data warehouse schema itself.
• Scriptella: Scriptella is an open-source ETL tool built for simplicity. Forget complex configurations – you can write your data transformation tools open source using familiar languages like SQL, directly within the tool. This makes Scriptella a user-friendly option, especially for those already comfortable with SQL or other scripting languages.
• Logstash: Logstash is an Open-Source Data Pipeline that extracts data from multiple data sources, transforms the source data and events, and loads them into ElasticSearch, a JSON-based search and analytics engine. It is part of the ELK Stack. The “E” stands for ElasticSearch, and the “K” stands for Kibana, a Data Visualization engine.

If you want a completely free solution, you can use a tech stack composed of Python, cron, and PostgreSQL, which will be an ETL pipeline. On top of that, it is robust, scalable, and easy to take on the cloud. Here’s a breakdown of why this stack is highly recommended:

• Python for Ingestion and Transformation: Python is a versatile programming language. It is supported by libraries like pandas, SQLAlchemy, and psycopg2, making it perfect for ingestion and transformation tasks. It is quite flexible for most data sources and formats.
• cron for Orchestration: cron is a time-based job scheduler in Unix-like operating systems that is very powerful in handling repetitive jobs. Easy setup and scheduling of your ETL jobs with cron can ensure the data pipeline runs problem-free at preset intervals.
• PostgreSQL for Storage and Additional Transformations: PostgreSQL, a powerfully reliable, open-source, flexible relational database, is the backbone of this stack. Its outstanding feature list and ability to store large amounts of data and run complex SQL-based transformations make it a secure choice for your data management needs.

This combination rarely disappoints and can address the requirements for nearly 85% of all your ETL jobs. Scaling this solution is not only cost-effective but also future-proof. Even if you’re not working with really “big data,” you won’t need more complex tools like Spark or Amazon Redshift. For instance, processing 20 million rows of data in this combination is completely feasible without distributed computing or MPP.

This open-source ETL stack offers a practical, scalable, and cost-effective solution for most data processing needs. It allows you to focus on your data rather than the complexities of the underlying infrastructure.

Checklist to Choose the Right Open-Source ETL Tool

While choosing the right tool for your business, ensure you check for the following points:

• Technical Expertise: Consider your team’s comfort level with coding and scripting requirements for different tools.
• Data Volume and Complexity: Evaluate the volume of data you handle and the complexity of transformations needed.
• Deployment Preferences: Choose between on-premises deployment for more control or cloud-based solutions for scalability.
• Budget Constraints: While open source data integration tools eliminates licensing fees, consider potential costs for infrastructure or additional support needs.

My Take

As you evaluate your data integration needs for the year ahead, the seven open-source free ETL tools highlighted in this post – Meltano, dbt, Airbyte, Apache Kafka, Pentaho Data Integration, Singer, and PipelineWise – each offer unique strengths and capabilities to consider. Whether you’re a small business looking for an easy-to-use solution, or an enterprise seeking advanced data orchestration and operations features, there is likely an option here that can help streamline your data workflows and make the most out of your data.

Till then, we wish you all the best in your journey to choose the right ETL open source tool.

In-Depth Analyses of Popular Open Source Tools

• PostgreSQL ETL Tools
• Python ETL Tools
• Top 12 Open Source Ci Cd Tools
• Top 10 Tableau Open Source

FAQ on Open-source ETL Tools

Additional Resources on Open-Source ETL Tools

1. ETL vs ELT
2. Best ETL Tools Data Warehouse
3. List out the Types of ETL Tools
4. Best Data Transformation Tools