Data Pipeline and Data Quality Questions

Designing, operating, and optimizing reliable data pipelines and ensuring data quality across ingestion, transformation, and consumption. Covers extract transform load and extract load transform patterns, efficient incremental and batch loading, idempotent processing, change data capture, orchestration and scheduling, and performance tuning to meet service level objectives. Includes data validation strategies such as schema enforcement, null and type checks, range and referential integrity checks, deduplication, handling late arriving and out of order data, reconciliation processes, and data profiling and remediation. Emphasizes observability, monitoring, alerting, and root cause analysis for data quality incidents, as well as data lineage tracking, metadata management, clear ownership and process discipline, testing and deployment practices, and governance to maintain data integrity for analytics and business operations. Also covers data integration concerns across customer relationship management systems, marketing automation systems, reporting systems, and other operational systems, including pipeline error handling, data contracts, and how test and validation checks can be integrated into pipelines to prevent regressions.