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DP-750 · 4.1.6 · Deploy and maintain data pipelines and workloads

Create Lakeflow pipeline

Create a data pipeline by using Lakeflow Spark Declarative Pipelines

Declare streaming tables, materialized views, flows, and dependencies in SQL or Python, then let Lakeflow manage execution.

See the pipeline graph

Microsoft Learn Lakeflow Pipelines Editor graph showing streaming tables and materialized views with dependencies and quality metrics.

Lakeflow derives execution dependencies from dataset references. The graph makes streaming tables, materialized views, and their upstream relationships visible.

Declare datasets, not step order

  1. 1SourceFiles, streams, or tables
  2. 2BronzeRaw maintained state
  3. 3SilverCleansed and conformed
  4. 4GoldConsumption-ready output
Declarative data flow

Definitions can be written in SQL or Python. The framework manages refresh order from the data graph.

Develop in the editor

Microsoft Learn Lakeflow Pipelines Editor showing source files, pipeline graph, execution controls, and Genie Code.

The editor supports multi-file development, dry runs, selective execution, previews, and pipeline-level monitoring.

Key points

  • Dataset declaration: Defines streaming tables or materialized views
  • Flow: Defines how data enters or changes a target
  • Dependency graph: Inferred from dataset references
  • Operations: Pipeline manages refresh, monitoring, and recovery

Exam trap

Do not manually sequence datasets whose dependencies the declarative graph can infer.

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