How do you enforce data integrity during ingestion into Clarity?

How do you enforce data integrity during ingestion into Clarity?

• A. Rely entirely on source data quality
• B. Validate only after data is loaded into reports
• C. Use schema validations, referential integrity, ETL/ELT checks, exception handling, and post-load reconciliation
• D. Disable constraints to maximize throughput

Answer: (C)

Enforcing data integrity during ingestion means layering checks and controls throughout the data pipeline so that bad data is detected and handled before it can affect downstream systems. The strongest approach combines schema validations (ensuring incoming data matches the defined structure, types, nullability, and value domains), referential integrity (preserving valid relationships between entities so references aren’t orphaned or inconsistent), ETL/ELT checks (applying business rules and transformations while catching anomalies, deduplicating records, and validating cross-field constraints during the transformation step), robust exception handling (capturing and routing errors to controlled paths with clear logs and retry/error management rather than letting problems slip through), and post-load reconciliation (comparing loaded data against source or expected results to verify completeness, accuracy, and consistency after the load). This multi-layer approach catches issues early, maintains data quality throughout ingestion, and provides a reliable foundation for reporting and analytics. Relying solely on source quality isn’t sufficient because problems can exist in sources, and validating only after loading into reports delays detection and can contaminate downstream processes. Disabling constraints removes essential safeguards, trading correctness for throughput and enabling inconsistent data to slip into the system.

Enforcing data integrity during ingestion means layering checks and controls throughout the data pipeline so that bad data is detected and handled before it can affect downstream systems. The strongest approach combines schema validations (ensuring incoming data matches the defined structure, types, nullability, and value domains), referential integrity (preserving valid relationships between entities so references aren’t orphaned or inconsistent), ETL/ELT checks (applying business rules and transformations while catching anomalies, deduplicating records, and validating cross-field constraints during the transformation step), robust exception handling (capturing and routing errors to controlled paths with clear logs and retry/error management rather than letting problems slip through), and post-load reconciliation (comparing loaded data against source or expected results to verify completeness, accuracy, and consistency after the load). This multi-layer approach catches issues early, maintains data quality throughout ingestion, and provides a reliable foundation for reporting and analytics. Relying solely on source quality isn’t sufficient because problems can exist in sources, and validating only after loading into reports delays detection and can contaminate downstream processes. Disabling constraints removes essential safeguards, trading correctness for throughput and enabling inconsistent data to slip into the system.