Lakeflow Declarative Pipelines developer reference

This section contains reference and instructions for Lakeflow Declarative Pipelines developers.

Data loading and transformations are implemented in Lakeflow Declarative Pipelines by queries that define streaming tables and materialized views. To implement these queries, Lakeflow Declarative Pipelines supports SQL and Python interfaces. Because these interfaces provide equivalent functionality for most data processing use cases, pipeline developers can choose the interface that they are most comfortable with.

The following pages provide an overview of and references for developing Lakeflow Declarative Pipelines code:

Topic	Description
Develop pipeline code with Python	An overview of developing Lakeflow Declarative Pipelines in Python.
Lakeflow Declarative Pipelines Python language reference	Python reference documentation for the dlt module.
Develop pipeline code with SQL	An overview of developing Lakeflow Declarative Pipelines in SQL.
Lakeflow Declarative Pipelines SQL language reference	Reference documentation for SQL syntax for Lakeflow Declarative Pipelines.
Manage Python dependencies for Lakeflow Declarative Pipelines	Instructions for managing Python libraries with Lakeflow Declarative Pipelines.
Convert Lakeflow Declarative Pipelines into a Databricks Asset Bundle project	Instructions for working with Databricks Asset Bundles with Lakeflow Declarative Pipelines.
Develop Lakeflow Declarative Pipelines code in your local development environment	An overview of options for developing Lakeflow Declarative Pipelines code locally.