Open-source Improvado alternatives

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.

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.