Core design principles
Every user experience and technical design decision adheres to these principles.
Open-source engine that comes with a custom notebook UI for building data pipelines.
- Mage comes with a specialized notebook UI for building data pipelines.
- Use Python and SQL (more languages coming soon) together in the same pipeline for ultimate flexibility.
- Set up locally and get started developing with a single command.
- Deploying to production is fast using native integrations with major cloud providers.
Build and deploy data pipelines using modular code. No more writing throwaway code or trying to turn notebooks into scripts.
- Writing reusable code is easy because every block in your data pipeline is a standalone file.
- Data validation is written into each block and tested every time a block is run.
- Operationalizing your data pipelines is easy with built-in observability, data quality monitoring, and lineage.
- Each block of code has a single responsibility: load data from a source, transform data, or export data anywhere.
Designed from the ground up specifically for running data-intensive workflows.
- Every block run produces a data product (e.g. dataset, unstructured data, etc.)
- Every data product can be automatically partitioned.
- Each pipeline and data product can be versioned.
- Backfilling data products is a core function and operation.
Analyze and process large data quickly for rapid iteration.
- Transform very large datasets through a native integration with Spark.
- Handle data intensive transformations with built-in distributed computing (e.g. Dask, Ray) [coming soon].
- Run thousands of pipelines simultaneously and manage transparently through a collaborative UI.
- Execute SQL queries in your data warehouse to process heavy workloads.