Features¶
Use this hub to explore the capabilities you can configure with the Lakeflow Framework (LFF). Some sections cover framework-native features, including metadata-driven specs, validation, Python extensibility, and quarantine handling. Others cover Databricks Lakeflow SDP product features that LFF exposes through the data flow spec, with links back to Databricks documentation where useful.
Start with a category below, or jump to the complete A–Z index when you already know the feature name.
Metadata-Driven Development
Spec-as-code — formats, templates, substitutions, and validation.
Open sectionAuthoring and Tooling
Editor and UI support — IntelliSense and Databricks UI integration.
Open sectionConfiguration Management
Org and pipeline defaults — framework config, secrets, Spark conf, operational metadata, logging, and build parallelization.
Open sectionSupported Sources and Targets
Platform source and target types configured in the data flow spec. Straddles Databricks types and LFF wiring.
Open sectionKey Databricks Features
Databricks / SDP product features LFF exposes through the spec — what we surface, how you configure it, and links to Databricks Docs.
Open sectionPython Development and Extensibility
Ship Python with the bundle — libraries, transforms, dependencies, and Python as a source.
Open sectionData Quality
Expectations (SDP) and framework quarantine for records that fail quality checks.
Open sectionEnvironments and Versioning
Logical environments and how specs and the framework are versioned.
Open sectionMigrations
Bring existing tables into Lakeflow Framework-managed pipelines.
Open sectionLooking for a specific feature by name? See Features A–Z.