TLDR
Mage is an open-source data pipeline tool for transforming and integrating data. Find out how Mage compares to open-source ELT alternatives like Airbyte. We’ll break down the features, pricing, pros, cons, and more.
Outline
About the products
Feature comparison
Next steps
About the products
Mage
is an open-source data pipeline tool for integrating (EL) and transforming (T) data. Mage was started in 2020 by engineers who worked on data and dev tools (e.g.
) at Airbnb for 5+ years. Their focus is to provide easy developer experience for building and managing data pipelines.
Airbyte
is an open-source ELT tool, created in July 2020. Their goal is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. They released a cloud offer in April 2022 with a new pricing model distinguishing database from APIs and files.
Feature comparison
Mage | Airbyte | |
Specialty | Data pipelines | ELT as a first step |
Number of sources | 307 (from Singer taps and targets) | More than 200 |
Number of destinations | 20 - all the main ones and more (from Singer taps and targets) | All data warehouses, lakes, and databases |
Customizations | Mage uses the data engineering community standard for data integrations called the Singer Specification. All connectors are written in Python. Mage provides tutorials, guides, examples, and training to create custom connectors. | User can edit any pre-built connectors and build new ones within 30 minutes with Airbyte’s Connector Development Kit. |
Database replication | Full table and incremental via CDC (change data capture) | Full table and incremental via CDC (change data capture). |
Integration with modern data stack | Integrate with any Python library. Mage has a native integration with DBT: preview DBT results, orchestrate DBT model runs, schedule DBT models to depend on non-DBT tasks (e.g. ETL/ELT pipelines). | Integrate with Kubernetes, Airflow, Prefect, Dagster, and DBT. Integrations can be contributed by the community. |
Support and developer documentation | Support is instantly provided through Slack ( ) and via email ( ) 1-on-1 tech support is provided over Zoom and can easily be schedule through Slack and email Documention ( ) has been praised by data engineering community | Airbyte provides in-app chat support with an average time to respond of 5 minutes. Their documentation is comprehensive and full of tutorials. Airbyte also has a Slack and Discourse community where help is available from the Airbyte team, other users or contributors. Airbyte does not provide any training services. |
Support service-level agreements (SLA) | Mage is self-hosted. Dedicated engineering support available upon request. | Available |
Security | SOC2 | SOC2, ISO 27001, GDPR |
Vendor lock-in | Mage is open-source. The code and connectors you use and write are always yours and available to you if you switch tools. Mage's technical design makes your code and connectors modular and interoperable. | Airbyte Core and Connectors are open-source |
Pricing | Free (self-hosted on AWS, GCP, Azure, or Digital Ocean) | Free (open-source) plan and volume-based pricing differentiating APIs from databases. Credits are rolled over. |
Specialty
Mage focuses on data pipelines while Airbyte focuses on ELT as a first step.
Number of sources
Mage currently offers 307 data source connectors while Airbyte provides 200+.
Number of destinations
With Mage, you can add all the main destinations and add additional destinations from an extensive collection of Singer taps and targets.
With Airbyte, you can add all data warehouses, lakes, and databases.
Customizations
Mage uses the data engineering community standard for data integrations called the Singer Specification. All connectors are written in Python. Mage also provides tutorials, guides, examples, and training to create custom connectors.
With Airbyte, users can edit any pre-built connectors and brand new ones within 30 minutes with Airbyte's Connector Development Kit.
Database replication
You can replicate full table and incremental via CDC (change data capture) for both Mage and Airbyte.
Integration with modern data stack
With Mage, you can integrate with any Python library. Mage has a native integration with DBT:
Preview DBT results
Orchestrate DBT model runs
Schedule DBT models to depend on non-DBT tasks (e.g. ETL/ELT pipelines)
Airbyte integrates with Kubernetes, Airflow, Prefect, Dagster, and DBT.
Support and developer documentation
Mage provides instant, real-time support via Slack (
) and via email (
). Mage also provides 1-on-1 tech support or group training services over zoom which can be easily scheduled through Slack or email. Last but not least, Mage offers rich and detailed documentation (
) with frequent updates, including migration support and example tutorials. It has been praised by the engineering community.
Airbyte provides in-app chat support (with average time to respond of 5 minutes). Their documentation is comprehensive and full of tutorials. Airbyte also has a Slack and Discourse community where help is available from the Airbyte team, other users, or contributors. Airbyte does not provide any training services.
Support service-level agreements (SLA)
Both have SLAs available, while Mage is self-hosted. Mage also offered dedicated engineering support available upon requeset.
Security
Both are SOC 2 compliant, while Airbyte is also ISO 27001 and GDPR compliant.
Vendor lock-in
Mage is open-source. The code and connectors you use and write are always yours and available to you if you switch tools. Mage's techincal design makes your code connectors modular and interoperable.
Airbyte Core and Connectors are open-source.
Pricing
Mage is free as long as you are self-hosted (AWS, GCP, Azure, or Digital Ocean).
Airbyte offers both a free (open-source) plan as well as premium plans which is volume-based pricing differentiating APIs from databases. Credits are rolled over.
Conclusion
Airbyte is distinguished by its priorities to support as many data source and destination systems as possible. This is done by using an open-source model for its connectors and actively encouraging its community to contribute.
Mage provides a unique and flexible approach to data transformation that includes orchestration. It was created to incorporate core design principles of easy developer experience, built-in engineering best practices, data as a first-class citizen, and scaling made simple.
Next steps
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