Open-source Keboola alternatives

Kestra is an orchestration platform, open source, for data pipelines, AI workflows and infrastructure automation. It brings scheduled and event-triggered automation together under one declarative interface that does not depend on a programming language, applying infrastructure-as-code practices to pipelines so that reliable workflows can be defined in a few lines of YAML.

Workflows can be built in the UI, written by a built-in AI Copilot, or generated from coding agents such as Claude Code and Cursor using agent skills, and everything can be kept as code with Git integration even when it was authored visually. A large plugin ecosystem connects it to external systems, and key concepts cover tasks, triggers and flows. The project is written in Java, with topics covering orchestration, high availability, pipeline-as-code and DevOps, and the 2.0 release adds new capabilities.

Kestra is Apache-2.0 licensed and can be self-hosted, with a quick start that gets a first workflow running in minutes. It targets data engineers, platform teams and automation developers who want an alternative to schedulers such as Airflow with a more declarative style.

Airbyte is an open-source data movement platform for moving data from APIs, databases and files into data warehouses, data lakes and AI applications. Its premise is that only an open-source project can reach the long tail of data sources while letting engineers tailor existing connectors, with the aim of moving data from any source to any destination.

It offers a catalog of 600 or more connectors for APIs, databases, warehouses, lakes and AI applications, with warehouse destinations such as BigQuery, Redshift and Snowflake, and topics also mention change data capture. The README distinguishes products by job: Airbyte Open Source or Airbyte Cloud for ELT and ETL into warehouses, lakes or databases, and Airbyte Agents, a managed data and context layer, plus an open-source Agent SDK for giving AI agents and MCP clients real-time access to business data.

Airbyte is written in Python and Java, and its license is listed as 'Other' on GitHub because the project uses a mix of licenses, so review the terms for your use. It can be self-hosted or run as the managed Airbyte Cloud, and suits data teams that need many integrations without writing each pipeline by hand.

dbt is a framework for transforming data in a warehouse using the same practices that software engineers apply to applications. Analysts and analytics engineers write models, and dbt parses, compiles and runs the project so that transformations are version-controlled and repeatable.

The repository now holds the Apache 2.0 source of dbt v2.0, a ground-up rewrite in Rust. According to its README, v2.0 parses and compiles projects much faster than v1, enforces a stricter language specification at parse time, produces Parquet artifacts alongside the existing JSON ones, and ships as one standalone executable, so no Python installation is needed. The local documentation experience was rebuilt around those artifacts to handle large projects.

The Apache 2.0 code is available to everyone, while the packaged dbt distribution adds dbt-specific customizations under a separate dbt product license. v2.0 supports macOS and Linux on x86-64 and ARM. The v1 Python implementation continues on the 1.latest branch. dbt Labs also offers dbt as a commercial cloud platform.

Mage, called Mage OSS in its README, is a self-hosted development environment for building data pipelines. It is meant for teams that automate ETL tasks, design data flows or orchestrate transformations, and it presents the work in a notebook-style interface made of modular blocks of code.

Pipelines are written block by block in Python, SQL or R. Jobs can be run manually or on a schedule, including cron expressions, and prebuilt connectors reach databases, APIs and cloud storage. Debugging is visual, with logs, live data previews and execution you can follow step by step, and dbt models can be built and run inside Mage. Topics on the repository also reference Spark, reverse ETL and machine learning workloads.

Mage OSS is licensed under Apache-2.0 and installs with Docker, pip or conda, with no cloud account required. For larger deployments the vendor offers Mage Pro, a commercial platform that adds enterprise orchestration, collaboration and AI-assisted workflows.

Bruin is a data pipeline tool that brings data ingestion, transformation and data quality into a single framework. Instead of stitching together separate tools for loading, modeling and testing, you describe a pipeline with SQL, Python or R assets and run it against the major data platforms, such as BigQuery and Snowflake.

Pipelines can ingest data from different sources, materialize tables and views, and build incremental tables. Python assets run in isolated environments using uv, Jinja templating cuts down on repetition, and built-in quality checks validate results. A dry-run mode validates a pipeline end to end, secrets are injected through environment variables, and a VS Code extension supports day-to-day development.

Bruin is written in Go, licensed under Apache-2.0 and distributed as a command-line tool that is easy to install. It runs on your local machine, an EC2 instance or GitHub Actions, so no dedicated orchestration server is required. It suits data engineers and analytics teams who want pipelines defined as code in a repository.

Duckle is an open-source ETL and ELT platform for teams that want their data pipelines running on their own infrastructure. You build pipelines on a visual canvas, in Python or in SQL, then ship the same file to your own server or cloud account, where a headless runner executes it on a schedule.

Pipelines compile to SQL on DuckDB and use all the cores available, so a larger machine runs them faster. The platform lists support for a large catalog of components, dbt, change data capture, data quality checks, reverse ETL and lineage, along with a web console, roles, an audit trail and an MCP server so AI agents such as Claude or Cursor can work with it. Each pipeline is a single file that can live in git.

Duckle is written in Rust and licensed under Apache-2.0, and it runs headless in Docker or on a plain server, with Kubernetes among its topics. It states there is no vendor cloud and no per-row billing. It is an independent project by SlothFlowLabs and is not affiliated with DuckDB Labs or MotherDuck. It suits data engineers who want a self-hosted alternative to managed pipeline services.