9 Best Open Source ETL Tools of 2026

1. 1

   Airbyte

   A data integration platform with 700+ connectors whose Airbyte Core engine is free to self-host, with Airbyte Cloud as the managed option.

   9.1overall

   A data integration platform with 700+ connectors whose Airbyte Core engine is free to self-host, with Airbyte Cloud as the managed option.

   Best forEngineers who want the widest catalog of ready-made connectors running inside their own VPC.

   Adoption data

   25,000Airbyte reports that more than 25,000 companies use it, including one in five Fortune 500 companies.

   Airbyte, Airbyte homepage: In production, at scale (2026): “25,000+ Companies 1 in 5 Fortune 500 companies”

   Airbyte is the closest thing to a self-hosted Fivetran. Airbyte Core runs in your own cloud or data center for free and ships the same 700+ connectors as the paid service, with change data capture, schema propagation and column selection. A Connector Builder covers internal APIs, and the Airbyte API, Terraform provider and PyAirbyte let you define pipelines as code. It plugs into Airflow, Dagster and Prefect when you want an outside orchestrator. If you would rather not run it, Airbyte Cloud Standard starts at $20 a month with 5 credits, about 1 GB of syncs, and Plus starts at $189 a month with 15-minute syncs and SSO. The vendor says more than 25,000 companies use it. Two caveats for lock-in-averse teams: Airbyte Core is licensed under the Elastic License 2.0, which allows use and modification but is not an OSI open source license, and governance features such as RBAC are reserved for paid plans. We compared documented features and published prices rather than running benchmarks.

   Strengths

   • +Largest connector catalog among the self-hostable tools here
   • +Same connectors on the free self-hosted Core and on Cloud
   • +Cheap managed entry point at $20 a month

   Limitations

   • −Airbyte Core uses the Elastic License 2.0, not an OSI-approved license
   • −SSO and role-based access sit in paid plans
   • −Pro and Enterprise Flex prices are quote-only

   SummaryAirbyte is the best pick when connector coverage is the bottleneck. Check that the Elastic License 2.0 fits your policy before standardizing on it.

2. 2

   Apache Airflow

   The Apache Software Foundation's Python platform to author, schedule and monitor data workflows.

   9.0overall

   The Apache Software Foundation's Python platform to author, schedule and monitor data workflows.

   Best forTeams that want an Apache 2.0 orchestrator to schedule extract, load and transform jobs across many systems.

   Adoption data

   80,000The Apache Airflow project said in 2025 that Airflow had more than 30 million monthly downloads and 80,000 organizations using it.

   Apache Airflow, Apache Airflow 3 is Generally Available (2025): “With over 30 million monthly downloads (up over 30x since 2020) and 80,000 organizations (up from 25,000 in 2020) now using Airflow, we’ve seen an incredible growth in pop”

   Apache Airflow is the default open source scheduler for data pipelines. You write each pipeline as Python code, template parameters with Jinja, and pick from operators for Google Cloud, AWS, Azure and many other services. The web UI shows the status and logs of every task, and the modular design uses a message queue so you can add workers as load grows. Airflow 3.4.0, released in November 2026, added task loops, dags written in other languages and support for AI agents. The project reported more than 30 million monthly downloads and 80,000 organizations using it in 2025, and its 2025 community survey drew 5,818 responses from 122 countries. It is licensed under Apache 2.0 by the Apache Software Foundation, so no single vendor controls it. Airflow does not ship a connector catalog like Airbyte, so most teams pair it with a loader such as Airbyte or dlt. If you do not want to run it yourself, Astronomer's Astro starts at $0.35 per deployment hour. We compared documented features and published prices rather than running benchmarks.

   Strengths

   • +Largest adoption of any open source orchestrator, with 80,000 organizations
   • +Apache 2.0 license under a neutral foundation
   • +Several managed services, so you are not tied to one host

   Limitations

   • −An orchestrator, not a connector catalog: you write or install the extract code
   • −Self-hosting means running a scheduler, workers and a metadata database
   • −Managed Astro costs add up because deployments run continuously

   SummaryAirflow is the safest long-term bet for scheduling ETL in your own infrastructure. Pair it with a loading tool rather than hand-writing every extract.

3. 3

   Apache Spark

   A multi-language engine for data engineering, data science and machine learning on single machines or clusters.

   8.9overall

   A multi-language engine for data engineering, data science and machine learning on single machines or clusters.

   Best forTeams transforming terabytes to petabytes of data in batch or streaming jobs.

   Adoption data

   80%The Apache Spark project says thousands of companies, including 80% of the Fortune 500, use Spark.

   Apache Spark, Apache Spark: Unified engine for large-scale data analytics (2026): “Thousands of companies, including 80% of the Fortune 500, use Apache Spark”

   Apache Spark is the transform engine behind much of big data ETL. It processes batch and streaming data with one API in Python, SQL, Scala, Java or R, and its distributed ANSI SQL engine uses Adaptive Query Execution to pick join strategies and reducer counts at runtime, which the project says speeds TPC-DS queries up to 8x. You can start with pip install pyspark on a laptop and run the same code on a cluster, on Kubernetes or through Spark Connect clients. The project says thousands of companies, including 80% of the Fortune 500, use Spark, and it has more than 2,000 contributors. Spark is free under the Apache License 2.0, so the cost is the compute you run it on. It is not a complete ETL product: it has no connector catalog, no scheduler and no UI for pipeline design, so teams pair it with Airflow, Dagster or Kestra for orchestration and with a loader for SaaS sources. We compared documented features and published prices rather than running benchmarks.

   Strengths

   • +The standard engine for large-scale transformation
   • +Over 2,000 contributors and wide vendor support
   • +Same code runs on a laptop or a cluster of thousands of machines

   Limitations

   • −No built-in connectors catalog or scheduler
   • −Cluster tuning takes real engineering time
   • −Overkill for small SaaS-to-warehouse syncs

   SummarySpark is the best open source choice for big data ETL. Use it for heavy transforms and keep a lighter tool for routine SaaS syncs.

4. 4

   dlt

   An Apache 2.0 Python library for building extract and load pipelines in code, with dltHub as the paid managed runtime.

   8.7overall

   An Apache 2.0 Python library for building extract and load pipelines in code, with dltHub as the paid managed runtime.

   Best forPython engineers who want ingestion as plain code that runs anywhere, from a notebook to Airflow.

   Adoption data

   16,000dltHub reports more than 16,000 companies loading data with dlt in production and over 6 million PyPI downloads a month.

   dltHub, dltHub homepage (2026): “16,000+ Companies loading data into databases with dlt in production”

   dlt takes a different approach from Airbyte: instead of a server with a UI, it is a Python library you add to your own code with pip install dlt. You write the extract step for any REST API, database or file, and dlt handles normalization, schema evolution and loading into warehouses such as Snowflake, Databricks, BigQuery or Microsoft Fabric. Schema and data contracts and PII redaction before load are available in the free library. Because a dlt pipeline is just Python, it runs in a notebook, a cron job, Airflow, Dagster or Kestra, which keeps you free of any one runtime. dltHub reports more than 16,000 companies in production and over 6 million PyPI downloads a month. The open source tier has limited verified connectors, so expect to write more extract code than with Airbyte. The managed dltHub product adds a runtime, observability and data quality dashboards for $12,000 a month on a 12-month minimum, aimed at larger teams. We compared documented features and published prices rather than running benchmarks.

   Strengths

   • +Apache 2.0 library that the vendor says will stay free
   • +No server to run: it is a library inside your own code
   • +Strong production adoption for a young project

   Limitations

   • −Few verified connectors in the open source tier
   • −Requires Python skills; there is no visual designer
   • −The managed dltHub plan starts at $12,000 a month

   Summarydlt is the lightest way to build code-first ingestion with no new server to run. Budget engineering time for sources it does not cover out of the box.

5. 5

   Kestra

   An Apache 2.0 orchestration platform that defines data and infrastructure workflows declaratively in YAML.

   8.5overall

   An Apache 2.0 orchestration platform that defines data and infrastructure workflows declaratively in YAML.

   Best forMixed teams that want declarative pipelines with both a code editor and a no-code editor.

   Adoption data

   40,000Kestra says more than 40,000 organizations run Kestra, including nearly 800 companies with over 1,000 employees.

   Kestra, Kestra homepage (2026): “More than 40,000 organizations run Kestra, including nearly 800 companies with over 1,000 employees.”

   Kestra is an orchestrator that describes each workflow in YAML rather than Python, which makes pipelines easy to review in pull requests and readable by people who do not write code. It ships both a code editor and a no-code editor, more than 2,600 plugins and event-driven or scheduled triggers, and each task can run SQL, Python, shell, dbt, Spark or Terraform. The open source edition runs on PostgreSQL or MySQL and has unlimited flows and executions, plus an MCP server for AI agents. Kestra says more than 40,000 organizations run it, including nearly 800 companies with over 1,000 employees, and its GitHub repository has passed 30,000 stars. The open core matters for lock-in: SSO, LDAP, SCIM, role-based access, audit logs and worker groups sit in the Enterprise Edition, sold as an annual per-instance subscription with no public price. Kestra Cloud is available by request. We compared documented features and published prices rather than running benchmarks.

   Strengths

   • +Apache 2.0 core with unlimited flows and executions
   • +Large plugin catalog covering ETL and infrastructure tasks
   • +Readable YAML that non-Python users can review

   Limitations

   • −SSO, RBAC and audit logs require Enterprise Edition
   • −Enterprise and Cloud prices are not published
   • −Smaller ecosystem of community answers than Airflow

   SummaryKestra is the best open source orchestrator for teams that prefer YAML over Python. Check which governance features you need before assuming the free edition is enough.

6. 6

   Dagster

   An asset-based data orchestrator with an open source core and the paid Dagster+ service, now part of Prefect.

   8.4overall

   An asset-based data orchestrator with an open source core and the paid Dagster+ service, now part of Prefect.

   Best forAnalytics engineering teams that want lineage, data quality checks and dbt orchestration in one tool.

   Adoption data

   16.4kDagster's main GitHub repository had 16.4k stars when checked in November 2026.

   GitHub, dagster-io/dagster repository (2026): “Stars 16.4k stars”

   Dagster organizes pipelines around the data assets they produce, such as tables and models, rather than around tasks. That gives you asset-level lineage, asset checks, freshness policies, partitions and backfills in the same tool that schedules your jobs, and its dbt and Snowflake integrations are first-class. The core orchestration framework is open source and can be self-hosted. Dagster+ adds branch deployments, catalog search and hybrid deployment, where your code runs on ECS, Kubernetes or Docker while Dagster runs the control plane; the Solo plan is $10 a month plus $0.040 per credit and Starter is $100 a month for up to 3 users, with a 30-day trial. In July 2026 Prefect announced it was acquiring Dagster, and the founder's letter says the open source project will keep being actively developed. Dagster does not publish a user count, so adoption is scored on its 16.4k GitHub stars and is conservative. We compared documented features and published prices rather than running benchmarks.

   Strengths

   • +Lineage and data quality built into the orchestrator
   • +Low entry price for the managed service at $10 a month
   • +Hybrid mode keeps compute in your own infrastructure

   Limitations

   • −Acquisition by Prefect adds roadmap uncertainty
   • −SSO, audit logs and cost tracking are Pro-only
   • −Credit-based billing needs estimating for busy pipelines

   SummaryDagster is the strongest open source choice when lineage and data quality matter as much as scheduling. Watch the Prefect integration roadmap before a large migration.

7. 7

   Apache NiFi

   An Apache Software Foundation system to route, transform and distribute data flows through a browser-based designer.

   8.3overall

   An Apache Software Foundation system to route, transform and distribute data flows through a browser-based designer.

   Best forTeams moving streaming, log and file data between many systems with full provenance.

   Adoption data

   Thousands of companiesThe Apache NiFi project says NiFi automates data pipelines for thousands of companies worldwide across every industry.

   Apache NiFi, Apache NiFi homepage (2026): “NiFi automates cybersecurity, observability, event streams, and generative AI data pipelines and distribution for thousands of companies worldwide across every industry.”

   Apache NiFi is built for moving data between systems continuously, not for nightly warehouse loads. You design flows in a browser by connecting processors, then change them at runtime without redeploying. Every piece of data carries provenance, so you can trace where it came from and what touched it, and the engine supports guaranteed delivery, back pressure and dynamic prioritization when downstream systems slow down. Security features include HTTPS with configurable authentication and multi-tenant authorization. MiNiFi agents in Java and C++ collect data at the edge and feed central NiFi clusters. The project says NiFi runs cybersecurity, observability, event stream and generative AI pipelines for thousands of companies, and NiFi 2.12.0 shipped in September 2026. It is free under Apache 2.0. The trade-off is operations: NiFi clusters need memory tuning and careful upgrades, and it is a weaker fit for SQL modeling than dbt-centric tools. We compared documented features and published prices rather than running benchmarks.

   Strengths

   • +Mature Apache project with releases through 2026
   • +Lineage for every piece of data that passes through
   • +Handles streaming and file flows that batch ELT tools skip

   Limitations

   • −Java-based servers need tuning and careful upgrades
   • −Not built for warehouse modeling or SQL transforms
   • −No official managed service from the project itself

   SummaryNiFi is the right open source tool for streaming and file-based flows that need full provenance. Pair it with a warehouse tool for modeling.

8. 8

   Meltano

   An open source extract and load platform built on Singer connectors, with your whole data stack defined as code.

   7.9overall

   An open source extract and load platform built on Singer connectors, with your whole data stack defined as code.

   Best forEngineers who want Singer taps, dbt and Git-based environments in one CLI-driven project.

   Adoption data

   5,500Meltano says more than 5,500 data professionals are part of its community on Slack and GitHub.

   Meltano, Meltano homepage (2026): “Join 5,500+ data professionals on Slack and GitHub”

   Meltano turns an extract and load stack into a Git project. You add Singer taps and targets from a hub of 600+ connectors, configure dbt for transforms and Elementary for validation, and promote changes between named dev and prod environments through pull requests. The Meltano SDK makes it practical to write Singer connectors for internal or niche sources, and the same project runs from the CLI, a UI, an API or an AI agent. Self-hosted Meltano is open source and free, with paid support options. Meltano Cloud bills by compute hours rather than rows, from a Starter plan with 200 compute hours a month, but no dollar prices are published. Meltano says its community counts more than 5,500 data professionals on Slack and GitHub, a smaller base than Airbyte or dlt, so adoption is scored conservatively. Singer connectors vary in quality, and the vendor itself notes that self-hosting fits teams with a dedicated DevOps crew. We compared documented features and published prices rather than running benchmarks.

   Strengths

   • +Pipelines live in Git as a versioned project
   • +Reuses the open Singer connector standard
   • +No row-based fees when self-hosted

   Limitations

   • −Singer connectors vary in quality and maintenance
   • −Meltano Cloud prices are not published
   • −The vendor says self-hosting suits teams with DevOps capacity

   SummaryMeltano suits engineers who want their ELT stack versioned in Git on open Singer connectors. Test the specific taps you need before committing.

9. 9

   Apache Hop

   An Apache open source platform for designing data pipelines and workflows on a visual canvas and running them on several engines.

   7.8overall

   An Apache open source platform for designing data pipelines and workflows on a visual canvas and running them on several engines.

   Best forTeams that prefer visual, low-code ETL design but want to run it on Spark, Flink or Dataflow.

   Adoption data

   1.5kThe Apache Hop GitHub repository had 1.5k stars when checked in November 2026.

   GitHub, apache/hop repository (2026): “Stars 1.5k stars”

   Apache Hop is a visual ETL designer in the tradition of Kettle: you drag transforms onto a canvas, connect them with hops, and preview rows at any step while the pipeline runs. It ships 250+ pipeline transforms, 80+ workflow actions and 40+ database dialects, with built-in support for PostgreSQL, Oracle, SQL Server, Snowflake, BigQuery, Redshift, Databricks, MongoDB, Kafka, Salesforce, SAP, S3 and SFTP. The strongest feature is portability: the same pipeline runs on the native engine locally, on a remote Hop Server, natively on Spark, or on Flink and Google Dataflow through Apache Beam, chosen as a run configuration rather than a rewrite. Projects and environments keep credentials outside pipeline files. Version 2.20.0 shipped on 2 November 2026 and needs Java 21. Hop is entirely Apache 2.0, with no paid tier. Its community is small, with about 1.5k GitHub stars, so adoption is scored conservatively. We compared documented features and published prices rather than running benchmarks.

   Strengths

   • +Fully Apache 2.0 with no paid edition
   • +Same pipeline runs on several engines without rewrites
   • +Familiar model for former Kettle and Pentaho users

   Limitations

   • −Much smaller community than Airflow or Spark
   • −Java 21 desktop and server setup
   • −Fewer SaaS API connectors than Airbyte

   SummaryHop is the best fully open source visual ETL designer, and a natural home for teams leaving Pentaho. Expect a smaller community when you need help.

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