The exercise focuses on constructing a defensive Spark ETL pipeline, designed to handle common data processing issues such as schema mismatches, corrupt records, and invalid values. This pipeline aims to ensure that all failures are documented, allowing for retries or quarantine for investigation, rather than silently failing. By redirecting rows that are not parsable instead of dropping them, users can ensure that no important data is lost during the ETL process. Comprehensive custom logging and auditing will be integral to managing and rectifying failures efficiently.
"In this exercise, you'll build a defensive Spark ETL pipeline that handles various data issues, ensuring robust processing and reliability. The focus will be on preventing silent failures."
"The pipeline will manage schema mismatches, corrupt records, null or invalid values, and failures in business logic, with a comprehensive logging strategy in place."
"Rows that can't be parsed are redirected to quarantine instead of being dropped, ensuring no data is lost, and facilitating thorough investigation into the issues."
"With custom logging and an audit trail, every failure will either be retried, logged for further analysis, or quarantined to prevent silent job failures."
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