databricks-pipelines

You can build streaming ETL pipelines on Databricks using Lakeflow Spark Declarative Pipelines to ingest, cleanse, and aggregate data from bronze to gold layers. The Skill supports declarative development in Python or SQL to define the entire workflow.

Enforcing data quality in a Databricks data pipeline is done by integrating data quality expectations directly within Lakeflow Spark Declarative Pipelines. This ensures data integrity is validated continuously throughout the ETL processing stages.

Yes, Databricks Lakeflow pipelines support both batch processing and streaming using Auto Loader and incremental refresh. This allows you to process continuous data streams and batch data efficiently within the same declarative framework.

You can develop declarative data pipelines using either Python or SQL. Lakeflow Spark Declarative Pipelines, formerly Delta Live Tables, allows you to define desired outcomes in both languages without relying on imperative processing steps.

Yes, building ELT workflows with Lakeflow Spark Declarative Pipelines supports Auto CDC and Materialized Views. These features facilitate capturing change data and maintaining aggregated states for downstream business intelligence.

Use Databricks Asset Bundles when you need to create a new project structure for a Lakeflow pipeline. It provides the foundational setup to begin developing declarative ETL workflows that process streaming and batch data.