Best Open-Source ETL Tool for Moving Data Between Apps
4 open-source options compared for ETL tool for moving data between apps, with live GitHub data on each.
Last updated Repo data refreshed 25 Oct 20264 options
Our pick · meltano
Our pick is Meltano because it provides a modern, code-first approach for building and managing data integration pipelines, particularly for API integrations. Meltano, a declarative code-first data integration engine, offers a flexible and developer-friendly experience for creating and maintaining data pipelines due to its declarative design and adherence to the Singer standard. While Apache NiFi is powerful for complex real-time data flow automation and Hop Orchestration Platform provides a visually-driven platform for orchestrating diverse data pipelines, Meltano's focused approach on direct API integration is more suitable for developers moving data between applications. Airbyte is listed for its large connector catalogue, but it is source-available (Elastic License 2.0), not open source, so it is not the pick.
Meltano vs NiFi vs Apache Hop vs Airbyte: side by side
★ 2,667 starsLicence: MITLast commit 23 Oct 2026Latest release 7 Oct 2026Python
Meltano is a declarative, code-first data integration engine for building data and machine learning-powered products, aiming to simplify API integrations.
Best for: Data teams and developers who prefer a modern, code-first approach for building and managing data integration pipelines, especially those involving API integrations and ML projects.
What's good
Offers a code-first and declarative approach, integrating well with developer workflows and version control.
Leverages the Singer standard, providing access to a broad ecosystem of taps for data extraction and targets for loading.
Designed to streamline the creation and maintenance of API integrations for various applications.
Focuses on data integration with potential for machine learning pipeline orchestration.
Watch out for
Requires comfort with command-line interfaces and YAML configuration, which may not suit all users.
Less visual than tools like Apache NiFi or Hop, potentially increasing the learning curve for non-developers.
Primarily focused on the Extract and Load (EL) phases, with transformation often handled separately.
Apache NiFi is a robust system designed for automating data flow between systems, offering a web-based user interface for creating, monitoring, and managing data pipelines.
Best for: Enterprises needing highly reliable, complex, and real-time data flow automation with granular control and strong security.
What's good
Provides a powerful visual interface for designing and managing complex, real-time data flows.
Offers guaranteed delivery, robust error handling, and data provenance tracking capabilities.
Features an extensible architecture that supports custom processors and integration points.
Includes strong security features for data access and flow management.
Watch out for
Can be complex to set up and configure, particularly for advanced or high-volume deployments.
Requires significant system resources (memory and CPU) for demanding data processing tasks.
The extensive feature set and unique dataflow paradigm contribute to a steeper learning curve.
★ 1,511 starsLicence: Apache-2.0Last commit 24 Oct 2026Latest release 12 Aug 2026Java
The Hop Orchestration Platform provides a visual and metadata-driven environment for data integration and orchestration, focusing on portability and extensibility.
Best for: Users seeking a highly portable, visually-driven, and metadata-centric platform for orchestrating diverse data pipelines and workflows.
What's good
Employs a metadata-driven approach that enhances reusability and simplifies the maintenance of data pipelines.
Supports highly portable data pipelines that can be executed across diverse environments and cloud platforms.
Features a visual designer for creating complex transformations and workflows without extensive coding.
Designed for robust orchestration of various data integration tasks and workflows.
Watch out for
Offers fewer pre-built connectors compared to more specialized ELT tools, potentially requiring custom development.
The visual design paradigm might be less flexible for developers accustomed to code-first tools.
Community support, while active, is smaller than some of the largest Apache projects.
Airbyte offers a platform for data movement in ELT pipelines and AI agents, connecting various data sources to warehouses, lakes, and AI applications, with both self-hosted and cloud options.
Best for: Organizations needing a comprehensive solution with a vast number of pre-built connectors for ELT, provided they are comfortable with a source-available license.
What's good
Provides an extensive catalog of pre-built connectors for a wide range of APIs, databases, and file formats.
Supports flexible deployment options, including self-hosted instances and cloud-managed services.
Designed for both ELT pipelines and integration with AI applications, offering broad utility.
Features a user-friendly interface for managing data synchronizations.
Watch out for
Source-available (Elastic License 2.0), not open source: the licence restricts how you may use it.
Can be resource-intensive to deploy and manage for self-hosted instances, requiring careful sizing.
Requires a solid understanding of data integration and pipeline concepts for effective configuration.
"Dormant" means no commit pushed to the default branch in the last 12 months. "Archived" means the maintainers have marked the repository read-only on GitHub. "Deprecated" means the project's README says it is deprecated or no longer maintained. Stars, licence, commit and release dates come from the GitHub API (GitLab for projects developed there) and are cached for up to 24 hours. On licences: a custom licence is a licence file that isn't a single standard licence; source-available licences let you read the code but restrict its use, so those projects are not open source. Check the linked licence file before use. The written analysis is AI-assisted and checked against that data.
Looking for a NiFi alternative?
NiFi fits continuous dataflows that operators change while they run, but its card above notes that it is resource-heavy and has a steep learning curve. For designed, versioned pipelines, such as loading a data warehouse, Apache Hop builds pipelines and workflows visually and processes data in batch, streaming or a mix of the two, on its own engine or on Apache Beam runners such as Spark and Flink. For code-first ELT, Meltano runs Singer taps and targets. Airbyte has a catalogue of more than 600 connectors, but it is source-available (Elastic License 2.0), not open source.
Apache Hop originated from the same code base as Kettle, better known as Pentaho Data Integration, and is now an independent Apache project. Comparing NiFi with Pentaho is usually a choice between NiFi's live dataflows and Kettle-style designed pipelines, and Hop is that second option in Apache form. Hop's documentation also covers importing existing Kettle projects.
No. Apache Hop originated from the same code base as Kettle (Pentaho Data Integration), but it is an independent Apache project with its own concepts, configuration and engines. Hop can import Kettle projects.
Meltano is built on Singer: Meltano Hub lists Singer taps and targets that Meltano runs. To move away from Singer connectors, Apache NiFi and Apache Hop use their own processors and transforms, and Airbyte uses its own connector catalogue, under a source-available licence (Elastic License 2.0).
What is an open-source alternative to Apache NiFi?
On this page, Apache Hop (Apache-2.0) and Meltano (MIT) are the open-source alternatives: Hop for visually designed batch or streaming pipelines, Meltano for code-first ELT. Airbyte is often considered too, but it is source-available, not open source.
Are Meltano, NiFi, Apache Hop and Airbyte actively maintained?
Meltano, NiFi, Apache Hop and Airbyte are active: each has had a commit in the last 12 months (most recent: Meltano on 23 Oct 2026, NiFi on 25 Oct 2026, Apache Hop on 24 Oct 2026, Airbyte on 24 Oct 2026). Status is checked against GitHub daily.
What licences do Meltano, NiFi, Apache Hop and Airbyte use?
Meltano: MIT; NiFi: Apache-2.0; Apache Hop: Apache-2.0; Airbyte: source-available (elastic license 2.0), so not open source. Check each project's licence file before use.
Which is easiest to learn: Meltano, NiFi, Apache Hop or Airbyte?
By our estimate (1 = easiest, 5 = steepest): Meltano 3/5, NiFi 4/5, Apache Hop 3/5, Airbyte 3/5. Meltano, Apache Hop and Airbyte share the gentlest learning curve. This is ExploreGit's AI-assisted estimate, not a measured figure.
