11 Best FREE Open Source ETL Tools in 2024

With data becoming central to decision making, having a scalable ETL framework is crucial. While commercial ETL tools provide comprehensive capabilities, they can be prohibitively expensive, especially when getting started. This is where open-source ETL alternatives excel by offering free toolkits to ingest, process and move data in flexible ways.

In this post, we have compiled this year’s top 11 open-source ETL tools. We will evaluate strengths of each tool across factors like connectivity to diverse data sources, transformation abilities, performance and scalability prowess. Whether you are a developer or enterprise ETL practitioner focused on building modern data warehouses, analytics pipelines, or machine learning datasets, there is an open-source ETL option to align with your use case.

Let’s start reviewing the 2024 landscape of leading free and open-source ETL tools to find the right fit!

Top 11 Popular Open-Source ETL Tools

Here is a comprehensive list of the Top 11 Popular Open-Source ETL Tools:

1. Hevo Data
2. Apache Camel
3. Airbyte
4. Apache Kafka
5. Logstash
6. Pentaho Kettle
7. Talend Open Studio
8. Singer
9. KETL
10. Apache NiFi
11. CloverDX

1) Hevo Data

Type: Enterprise

Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. You can replicate data in near real-time from 150+ data sources to the destination of your choice, including Snowflake, BigQuery, Redshift, Databricks, and Firebolt.

For the rare times things do go wrong, Hevo ensures zero data loss. To find the root cause of an issue, Hevo also lets you monitor your workflow so that you can address the issue before it derails the entire workflow. Add 24*7 customer support to the list, and you get a reliable tool that puts you at the wheel with greater visibility.

Sign up here for a 14-Day Free Trial!

Hevo was the most mature Extract and Load solution available, along with Fivetran and Stitch but it had better customer service and attractive pricing. Switching to a Modern Data Stack with Hevo as our go-to pipeline solution has allowed us to boost team collaboration and improve data reliability, and with that, the trust of our stakeholders on the data we serve.

– Juan Ramos, Analytics Engineer, Ebury

Key features of Hevo Data

• Data Deduplication: Hevo deduplicates the data you load to a database Destination based on the primary keys defined in the Destination tables.
• Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
• Data Transformation: Hevo supports Python-based and drag-and-drop Transformations to cleanse and prepare the data to be loaded to your Destination.
• Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.

Check the Ebury Success Story on how Hevo empowered Ebury to build reliable data products.

Pricing

Hevo has a simple, and transparent Hevo Pricing model with 3 usage-based plans starting with a free tier, where you can ingest upto 1 million records.

Hevo Resources

Documentation

Learn more about Hevo

2) Apache Camel

Type: Open source

Apache Camel is an Open-Source framework that helps you integrate different applications using multiple protocols and technologies. It helps configure routing and mediation rules by providing a Java-object-based implementation of Enterprise Integration Patterns (EIP), declarative Java-domain specific language, or by using an API.

Apache Camel uses more than 100 components including FTP, JMX, and HTTP. It uses Uniform Resource Indicators (URI) to provide information such as which components are being used, the context path, and which options are applied on what components. 

Key features of Apache Camel

• Camel supports integrating across different transports like HTTP, JMS, TCP/IP, file systems easily, enabling routes between disparate systems.
• Out-of-the-box components allow connecting to APIs like AWS, Kafka, databases, queues, ERPs Route building is simplified for messaging patterns like multicast, load balancing, failover handling etc. with simple DSL.
• Handles failures transparently allowing retries, exponential backoffs along with mechanisms like dead letter channels.
• Transforms payloads easily between transport protocols and formats (XML, JSON, CSV etc) for system interconnectivity through its ‘Type Converter’ registry.
• Coordinates and manages parallel routing processing steps with proper sequencing, improving process work-flows.
• Performance metrics monitoring dashboards, capabilities like on/off toggling of routes, graceful shut-downs.

Resources

Documentation | Community | GitHub

3) Airbyte

Type: Open source

Airbyte is one of the Open-Source ETL Tools that was launched in July 2020. It differs from other ETL tools as it provides connectors that are usable out of the box through a UI and API that allows community developers to monitor and maintain the tool.

The connectors run as Docker containers and can be built in the language of your choice. By providing modular components and optional feature subsets, Airbyte provides more flexibility. 

Key features of Airbyte

• Can connect to > 30 protocols like MySQL, PostgreSQL, BigQuery, Snowflake, Salesforce APIs, JSON files, allowing pulling data from diverse sources.
• As a cloud-native application, Airbyte is designed for scalability, resilience and deployment simplicity on Kubernetes, AWS, GCP infrastructure.
• Airflow UI allows configuring transformations like aggregations, unioning tables, data normalization across sources.
• Incremental data sync capability allows regular, automated pipeline runs without worries of duplicate data.
• Can load processed data to data warehouses (Snowflake, Redshift), data lakes (S3, GCS), analytics tools (Looker, Tableau) enabling broad access.
• Advanced dashboard for monitoring records processed, pipeline performance metrics, errors for debugging data flows.
• Open-source model allows contributing new connectors, transformations, bug fixes to evolve product capabilities continuously.

Pricing

Currently, Airbyte has 3 pricing models: Community, Standard, and Enterprise depending on the number of connectors, the number of seats needed and the number of premium features activated.

Resources

Roadmap | Discourse | Documentation

4) Apache Kafka

Type: Open source

Apache Kafka is an Open-Source Data Streaming Tool written in Scala and Java. It publishes and subscribes to a stream of records in a fault-tolerant manner and provides a unified, high-throughput, and low-latency platform to manage data.

Apache Kafka can be used as a message bus, a buffer for systems and events processing, and to decouple applications from databases for both OLTP (Online Transaction Processing) and Data Warehouses.

Key features of Apache Kafka

• Kafka handles large data volumes with high throughput streaming operation required in ETL pipelines. Its distributed system scales horizontally.
• Kafka Connect framework allows easy integration with databases, AWS S3, other data sources as needed for data ingestion in ETL.
• Kafka Streams API allows creating stream processing applications that transform data in real-time as it flows in which suits ETL needs.
• Its distributed architecture ensures no single point of failure. Data is replicated across brokers providing inherent failure management.
• Kafka integrates easily with cloud data warehouses like Snowflake, Azure Synapse Analytics into which transformed data can be loaded after extraction and processing through Kafka.
• Kafka provides a Java/Scala SDK for programming and CLI tools for lifecycle management of scalable ETL pipelines.
• So Kafka delivers core ingestion, transformation and routing capabilities that underly the Extract, Transform and Load needs along with integration endpoints useful for open source ETL implementations.

Resources

Documentation | Slack

5) Logstash

Type: Open source

Logstash is an Open-Source Data Pipeline that extracts data from multiple data sources and 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.

It is written in Ruby and is a pluggable JSON framework that consists of more than 200 plugins to cater to the ETL process across a wide variety of inputs, filters, and outputs. It can be used as a BI tool or even as a Data Warehouse.

Key features of Logstash

• Logstash provides over 200 plugins for integrating with a wide variety of sources – databases, APIs, clouds, queues etc. This helps in extracting diverse data.
• Ability to parse different formats and transform data using filters and plugins for needs like deduplication, geoip lookup, translations etc.
• Ships data to Elasticsearch, splunk, AWS S3, Kafka, MySQL among 100 other possible outputs across APIs, clouds and analytics tools.
• With event-driven pipelining, Logstash scales horizontally to manage high event throughput required in large ETL scenarios.
• Pipelines can be defined in concise configs, shared, and edited centrally for maintainability without affecting runtime flows.
• Instrumentation allows tracking events end-to-end providing runtime visibility and error diagnostics.
• Open source plugins enable customizing Logstash functionality like inputs, filters, codecs, outputs based on integration needs.

Pricing

Currently, Logstash is part of ElasticSearch and comes in 4 pricing packages, namely Standard, Gold, Platinum, and Enterprise. The Standard edition is $95 per month, the Gold edition is $109 per month, the Platinum edition is $125 per month and the Enterprise edition is $175 per month.

Logstash

Documentation | Slack

6) Pentaho Kettle

Type: Open source

Pentaho Kettle is now a part of the Hitachi Vantara Community and provides ETL capabilities using a metadata-driven approach. This tool allows users to create their own data manipulation jobs without writing a single line of code. Hitachi Vantara also offers Open-Source BI tools for reporting and Data Mining that work seamlessly with Pentaho Kettle.

Key features of Pentaho Kettle

• User-friendly drag-and-drop interface to visually construct ETL workflows involving steps like data extraction, validation, integration, loading without coding.
• Connectors allow ingesting data from disparate sources like CSV, JSON, databases, warehouses, APIs, Hadoop, Kafka, S3.
• GUI-based transformations building for needs like filtering, aggregations, pivoting, validating data quality after extraction.
• Kettle workloads distribute across clusters allowing handling large volumes of data flows in ETL pipelines.
• Plugins allow extending functionality with custom Java code along with JavaScript steps for needs not met by built-in components.
• Automated capturing and injection of metadata like field datatypes for warehouse optimization.
• Advanced capabilities like scheduling, monitoring, workload balancing, error handling available out-of-the-box.

Pricing

Currently, Pentaho Kettle provides a 30-day free trial period. The exact pricing details are not disclosed.

Open source CI/CD tools are designed to automate the workflow from code check-in by developers to the release of code into production environments.

Resources

Documentation | StackOverflow

7) Talend Open Studio

Type: Open source

Talend Open Studio is a free and Open-Source ETL Tool that provides its users a graphical design environment, ETL and ELT support, and enables them to export and execute standalone jobs across runtime environments. It has a wide range of connectors for RDBMS, SaaS, Packaged applications, Dropbox, LDAP, FTP, and many more. It also offers Open-Source solutions for Data Preparation and Data Quality.

Key features of Talend

• Talend offers a holistic solution encompassing data integration, data quality, data governance, and master data management, catering to a wide range of data needs.
• Embrace flexibility with drag-and-drop visual tools for beginners or leverage Talend Open Studio’s code-based approach for advanced users and customization.
• Connect effortlessly with over 900 data sources, including databases, applications, files, and cloud platforms, breaking down data silos and facilitating seamless integration.
• Talend provides a rich library of pre-built and user-defined data transformation components to cleanse, enrich, and restructure data, ensuring its accuracy and usability.
• Maintain data integrity with Talend’s data quality tools, including profiling, cleansing, and rule-based validation, guaranteeing accurate and reliable data insights.

Pricing

Currently, Talend offers 4 pricing models. These include Stitch, Data Management Platform, Big Data Platform, and Data Fabric.

Resources

Documentation | StackOverflow

8) Singer

Type: Open source

Some Open-Source ETL Tools have a command line interface. Singer is one such tool that uses a command-line interface to allow users to build modular ETL Pipelines using its “Tap” and “Target” modules. Singer provides a framework that allows users to connect data sources to storage locations directly.

With a large collection of pre-built taps, scripts can be defined for ETL processes and users can write concise, single-line ETL processes that can easily be modified by swapping taps and targets.

Key features of Singer

• Singer is designed to be simple, lightweight, and easy to use for basic ETL tasks. It does not attempt to be a full-fledged ETL platform.
• Singer introduces the concept of Taps which are reusable packages for extracting data from different data sources like databases, REST APIs, file systems etc.
• Targets are Singer abstractions for loading data into various destinations.
• Singer uses a standard JSON-based schema to represent data during transfer between sources, transformations and destinations.
• Singer natively supports incremental extraction and syncing of data based on state files and bookmarks.
• Singer taps and targets can be extended or modified easily to integrate more data sources/targets. Developers can build custom taps and targets.
• Singer promotes a microservices approach where taps, targets and transformations are independent services that aid cloud deployments.

Resources

Documentation | Slack | Roadmap

9) KETL

Type: Open source

KETL is a production-ready ETL platform designed to assist the development and deployment of Data Integration processes. It allows users to use an Open-Source platform to manage complex data. The KETL engine consists of a multi-threaded server to manage different job executors. Job executors fall into several categories including SQL, OS, XML, Sessionizer, and Empty.

Key features of KETL

• Connectors for a variety of data sources – RDBMS, NoSQL, object storage, message queues, etc
• Support for different file formats like JSON, XML, AVRO, Parquet
• Data transformation capabilities like aggregations, joins, sorting, deduplication, etc.
• Optimized for performance through partitioning, pushes down, distributed execution
• Cloud-native implementation leveraging docker and orchestrators like Kubernetes
• Scalable to handle large volumes of data using big data technologies
• Real-time data ingestion support through change data capture and messaging
• Monitoring, logging, and reporting capabilities around data pipelines
• Metadata management for data discovery, lineage, and impact analysis
• Automated deployment options and infrastructure-as-code integrations
• Modular architecture allows customization and extensions of capabilities

10) Apache NiFi

Type: Open source

Apache NiFi allows you to automate and manage the flow of information systems. It also enables NiFi to be an effective platform for building scalable and powerful dataflows. NiFi follows the fundamental concept of Flow-Based Programming. It has a highly configurable web-based UI, and houses features such as Data Provenance, Extensibility, and Security features.

Key features of Apache NiFi

• NiFi offers a user-friendly drag-and-drop interface that allows you to create, visualize, and manage data flows without writing code. This visual approach simplifies the development and maintenance of complex data pipelines.
• NiFi excels at managing the movement and processing of data between systems. It handles scheduling, prioritization, queuing, data provenance, and back pressure control for efficient and reliable data flow.
• NiFi provides a rich set of built-in processors for data ingestion, transformation, routing, filtering, merging, splitting, joining, and more. These processors can be combined to create diverse data flows to meet specific needs.
• NiFi is designed for horizontal scalability, meaning you can add more nodes to handle increased data volumes and ensure continuous availability. It also features clustering capabilities for fault tolerance and load balancing.
• NiFi takes data security seriously, offering secure communication and multi-tenant authorization to protect sensitive information. It also supports encryption, access controls, and auditing features for robust data governance.
• NiFi meticulously tracks the lineage of every data element, providing a detailed history of its origin, transformations, and destination. This capability is crucial for data auditing, troubleshooting, and regulatory compliance.

A wide range of tools is available in the market for businesses seeking to replicate data. They have the option to select from either paid solutions or free open-source data replication tools. Discover the Best Open Source Data Replication Tools for 2024.

Resources

Documentation | Slack

11) CloverDX

Type: Open source

CloverDX is one of the first Open-Source ETL Tools. It has a Java-based Data Integration framework that is designed to transform, map and manipulate data of various formats. It can be used as a standalone system or be embedded with other databases and files such as RDBMS, JMS, SOAP, HTTP, FTP, and many more.

Key features of CloverDX

• It’s not just an ETL tool; CloverDX offers a comprehensive data integration platform encompassing data extraction, transformation, loading, orchestration, and governance.
• Similar to Talend, CloverDX provides a user-friendly drag-and-drop interface for designing data pipelines, making it accessible to both technical and non-technical users.
• Beyond the designer, CloverDX offers a server for automated pipeline execution and a self-service data transformation interface (Wrangler) for business users.
• Go beyond basic filtering and sorting with functions for aggregation, merging, data quality checks, and custom scripting.
• CloverDX prioritizes data quality and lineage tracking, ensuring accuracy and compliance with built-in validation rules and detailed history of data transformations.
• Embrace flexibility with support for various databases, cloud platforms, and custom connectors. You can even extend CloverDX functionality with custom components.

Pricing

Currently, CloverDX has 3 pricing models, Standard, Plus and Enhanced. You can talk to CloverDX Tech Support in case you face any issues.

Resources

Documentation | Forum for doubts

How to Choose the Best Open-Source ETL Tools?

Check out the following points when choosing the Best Open-source ETL Tool for your organization:

• Number of Connectors: Check if the tool covers the data source and destination that you are looking for. If the tools does not have your particular connector, check if the tool is extensible and can connect to new data sources.
• Target Audience: Find out the target audience for the tool. Check out the tool’s language and use cases. Some tools are built for developers who require extensive coding knowledge. In that case, go for a no-code data integration tool that supports GUI and a drag-and-drop interface.
• User Friendliness: Every tool gives a free trial, use the tool for a few days and and figure out if it is meant for your requirements.
• Customization: Customizability varies among open-source ETL tools, particularly in extractor features. For instance, consider batch processing or extraction filtering to manage heavy data loads. Assess the tool’s adaptability to ensure it meets your specific requirements.
• Data Transformation Functionalities: Open-source ETL tools usually offer very limited features when it comes to data transformation. Make sure that the open-source ETL tool offers a full set of flexible transformations.
• Scalability: Make sure that the tool is flexible enough to grow with your data loads.
• Security Check: Open-source free tools are often negligent of security. Check out if the tool is GDPR-compliant to keep your data safe in transit.

Comparing Open Source ETL Tools

ETL Tools	Format Supported	Sources supported	Automation	Codeless/ Code-based	Installation & Deployment	Subscription
	Multiple data formats	More than 150 plug-and-play connectors—including file systems, databases, and SaaS applications.	Yes	Codeless	On-premises and cloud-based	Free for starter, the rest  available on the website
	JSON, XML, SOAP, ZIP, and more (50+ types)	Spring, Quarkus, and CDI	Yes	Code-based	On-premises and as an embeddable library	Free
	CSV, JSON, Excel, Feather, Parquet and more	Can connect to > 30 protocols	Yes	Low Code/No Code	On-premises and cloud-based	Depeonds on the number of connectors
	Event-record format	connects to hundreds of event sinks and sources, such as Amazon S3, Postgres, JMS, Elasticsearch, and more.	Yes	Codeless	able to be implemented on cloud, on-premises, in virtual machines, and containers	Free
	XML, JSON, CSV, logs, and more	Cloud platforms, Kubernetes, Confluence, and CRMs	Yes	Codeless	On-premises and cloud-based	Free
	Multiple data formats	Java-based libraries	Yes	Codeless	On-premises	Enterprise Edition/community Project
	All big data formats	RDMS,SaaS connectors, CRMs	Yes	Codeless	On-premises and cloud-based	Free
	Multiple sources	Python-based libraries	Yes	Code-based	Virtual environment or on-premises	Free
	JSON, XML, AVRO, Parquet	variety of data sources	Yes	Codeless	On-premises and cloud-based	Available on the website
	JSON, XML, Avro, Parquet, Apache Thrift,CSV, HL7, Protocol Buffers,Apache ORC, Grok patterns	Apache Kafka, Apache Hadoop, Amazon S3, MongoDB,  Elasticsearch	Yes	codeless	able to be implemented on cloud, on-premises, in virtual machines, and containers	Available on the website
	All data formats	All 3rd party Java libraries	Yes	Codeless	On-premises and cloud-based	Available on the website

Conclusion

This article gave a comprehensive list of the Top 11 Open-Source ETL Tools. It further explained the features and pricing models for a few of the tools. Finally, it highlighted some of the limitations of these tools. Overall, Open-Source ETL Tools play a pivotal role in the field of Data Analytics today due to their regular development and cheaper prices.

Paid ETL Tools are also important as they provide better features and insights from their customers.  At the end, whether you opt for a Paid ETL Tool or an Open-Source Tool, you can be rest assured that the quality of your data will never get compromised.

You can now also learn about the best ETL tools that are currently available in the market. Based on your requirements, you can leverage one of these to boost your productivity through a marked improvement in operational efficiency.

Learn more about the concept of ETL and ETL tools with these essential reads:

• ETL Incremental Loading 101: A Comprehensive Guide
• 9 ETL Best Practices and Process Design Principles
• 9 Best Python ETL Tools in 2024

Basic Doubts Related to Open-source ETL tools

What is ETL?

ETL(Extract, Transform, Load) is a data integration process that combines data from multiple sources, transforms it, and then loads it into a central repository that can be a data warehouse or database.

• Extract: In the extraction phase, data is collected from various source systems, which can include databases, applications, files, APIs, and more.
• Transform: Once the data is extracted, it undergoes a series of transformations to prepare it for the target system. Transformations may include cleaning, filtering, aggregating, and structuring data to meet business requirements.
• Load: In the loading phase, the transformed data is loaded into the target system, which could be a data warehouse, database, or any other destination chosen for analysis or storage.

What are the types of ETL Tools?

1. Enterprise ETL Tools: Enterprise ETL tools are used by large organizations that handle a much larger volume of data from multiple sources. These tools have unique features that can handle complex data transformations and automate the ETL process.
2. Open-source/Free ETL Tools: Open-source ETL tools are freely available and accessible tools that can be used and tailored for specific requirements. The source code of such tools is publicly accessible, and data analysts can analyze and modify the tool to enhance their ETL process.
3. Custom ETL Tools: Custom ETL tools are tailored to an organization’s needs and crafted using general-purpose programming languages such as Python, SQL, and Java, along with technologies like Kafka, Hadoop, and Spark. While offering flexibility, they demand substantial effort, involving the manual creation of data pipelines. Organizations using custom ETL tools are responsible for maintenance, documentation, testing, and ongoing development.
4. ETL Cloud Services: Cloud ETL services empower organizations to swiftly and effectively execute ETL operations within a cloud computing environment. Certain ETL cloud services are proprietary and exclusive to the respective cloud vendor’s framework, making them incompatible with other cloud platforms.

What are the limitations of Open-Source ETL Tools?

• Enterprise Application Connectivity: Companies are not able to connect a few of their applications with Open-Source ETL Tools.
• Management & Error Handling Capabilities: Open-Source ETL Tools are not able to handle errors easily due to their lack of error handling capabilities.
• Non-RDBMS Connectivity: Some Open-Source ETL Tools are not able to connect with a variety of RDBMS and can hamper the performance of the Data Pipeline when data is collected from these data sources.
• Large Data Volumes & Small Batch Windows: Some Open-Source ETL Tools need to analyze large data volumes but can process the data in small batches only. This can reduce the efficiency of the Data Pipeline.
• Complex Transformation Requirements: Companies that have complex transformation needs cannot use Open-Source ETL Tools. This is because they often lack support for performing complex transformations.
• Lack of Customer Support Teams: As Open-Source ETL Tools are managed by communities and developers all around the world, they do not have specific customer support teams to handle issues.
• Poor Security Features: Being Open-Source causes these tools to have poor security infrastructure and become prone to many cyber attacks.

In case you want to integrate data into your desired Database/destination, then Hevo’s data pipelines are the right choice for you!

It will help simplify the ETL and management process of both the data sources and the data destinations.

Want to take Hevo for a spin?

Sign Up for a 14-day free trial here and experience the feature-rich Hevo suite first hand.

Share your experience of learning about the popular Open-Source ETL Tools in the comments section below!