Declarative pipelines and dependencies

Lakeflow pipelines use the Apache Spark Declarative Pipelines framework to define data transformations in SQL or Python. A declarative pipeline describes datasets and dependencies; the system plans execution and manages pipeline operations. This differs from manually sequencing every table write in notebook code, though you still need to design correct definitions, expectations, and source handling. Use clear dataset names and keep each transformation focused. Understand the distinction between a streaming table for incremental processing and a materialized view for maintained query results based on its definition and supported semantics. Features and syntax can evolve, so check current documentation and runtime support. Practice task: sketch dependencies from a raw event source through validated events to a daily summary. Applying the pattern: Define source and target datasets, express transformations as dependencies, validate the pipeline graph, then monitor updates and failures. Checks and failure modes: A declarative framework does not make an incorrect join, data contract, or quality rule correct automatically. Before production, test both a representative input and a boundary case. Confirm the expected row grain, permissions, recovery behavior, and observable output for the task. Record the configuration needed to reproduce the run and compare its result with the agreed data contract. Practice task: What does a declarative pipeline primarily describe?