Iterate on your data platform with confidence by leveraging software development best practices. Version control your pipelines, configurations, and assets in Git. Learn whether a change in one tool broke another before going live with automatic end-to-end testing in CI. Manually verify the impact of any changes in isolated staging and development environments.
A generic plugin architecture and abstractions around installation, configuration, integration, and deployment allow you to build your ideal data platform out of your chosen data tools and technologies while offering a streamlined, cohesive experience.
As platforms and organizations grow, the requirements for data tools change and your data platform should be able to evolve and scale with ease. Meltano plugins can easily be added or replaced so that you can find and use the right tool for the job and unlock new capabilities without ever being locked into past decisions.
Bring your whole team together in one project that defines every aspect of your data platform. People across disciplines can easily see what others are up to and suggest changes in areas they otherwise would never have seen or would have been afraid to touch, knowing that checks are in place to prevent accidental breakage.
Package management for all your data stack tools, with all installation differences abstracted away. Discover tools on MeltanoHub, add them with a simple command, and introduce arbitrary Python tools into your project as utilities.
Configure all your tools with the same commands and set different values in development, staging, and production environments. Manage sensitive values through environment variables, and version control all others in your Git repository.
Run representative copies of your entire data platform in different isolated environments so you can experiment freely and validate your changes in development before moving to CI, and in staging before promoting to production.
Everything you need to manage your Singer incremental replication state, table/column selection catalogs, and configuration. Manage multiple instances of the same extractor or loader with configuration inheritance and overrides.
Composable cross-plugin scheduled pipelines allow you to run different plugins together on the orchestrator of your choice. Easily run dbt and Great Expectations along with Singer taps and targets with automatic warehouse connection configuration.
All of your tools live together in a single repository. Changes across components can be reviewed in one pull request. End-to-end testing in CI with `meltano test`. Containerize your entire data platform into one Docker image. Automate deployment to different environments with CI/CD.
