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A data engineer is designing an ETL pipeline to process both streaming and batch data...

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A data engineer is designing an ETL pipeline to process both streaming and batch data from multiple sources The pipeline must ensure data quality, handle schema evolution, and provide easy maintenance. The team is considering using Delta Live Tables (DLT) in Databricks to achieve these goals. They want to understand the key features and benefits of DLT that make it suitable for this use case.

Why is Delta Live Tables (DLT) an appropriate choice?

A.

Automatic data quality checks, built-in support for schema evolution, and declarative pipeline development

B.

Manual schema enforcement, high operational overhead, and limited scalability

C.

Requires custom code for data quality checks, no support for streaming data, and complex pipeline maintenance

D.

Supports only batch processing, no data versioning, and high infrastructure costs

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A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
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