Explanation:
Delta Live Tables, currently called Lakeflow Spark Declarative Pipelines, provides expectations for declarative data-quality rules. Expectations can record pass/fail metrics and optionally warn, drop invalid records, or fail the pipeline. Unity Catalog governs access, Delta Lake provides transactional storage, and Auto Loader handles ingestion.
A pipeline’s source data quality is declining, and the data engineer wants to automate data-quality monitoring. Which tool should be used?
A
Unity Catalog
B
Data Explorer
C
Delta Lake
D
Delta Live Tables, now Lakeflow Spark Declarative Pipelines
E
Auto Loader
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