How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks documentation explains that Lakeflow Declarative Pipelines build upon Structured Streaming but add higher-level orchestration and automation capabilities. They automatically manage dependencies, materialization, and recovery across multi-stage data flows without requiring external orchestration tools such as Airflow or Azure Data Factory. In contrast, Structured Streaming operates at a lower level, where developers must manually handle orchestration, retries, and dependencies between streaming jobs. Both support Delta Lake outputs and schema evolution; however, Lakeflow Declarative Pipelines simplify management by declaratively defining transformations and data quality expectations. Hence, the correct distinction is A --- automated orchestration and management in Lakeflow Declarative Pipelines.
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