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Answer: Leveraging Databricks‘ Expectation Framework to define and execute data quality constraints
Leveraging Databricks‘ Expectation Framework is the most suitable approach for automating data quality checks within Apache Spark pipelines in Databricks. This framework allows for the definition and execution of data quality constraints, such as checks for null values, data types, and ranges, enabling automatic validation of data against predefined rules. It offers a flexible and scalable solution for identifying and isolating problematic data efficiently. While developing a UDF (Option B) is possible, it requires more manual effort and maintenance. Implementing Spark SQL window functions (Option A) is less efficient for this specific purpose, and using a separate Spark job (Option D) lacks the real-time integration provided by the Expectation Framework.
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Ensuring high data quality is essential for your processing pipeline. How can you automate data quality checks within your Apache Spark pipelines in Databricks to detect and separate corrupt or missing data?
A
Implementing Spark SQL window functions to compare records and identify anomalies
B
Developing a UDF (User Defined Function) that validates data against predefined rules and filters out invalid records
C
Leveraging Databricks‘ Expectation Framework to define and execute data quality constraints
D
Utilizing a separate Spark job that runs data quality reports and alerts the team on issues
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