When data quality issues are causing downstream reports or ML models to produce wrong results.
You are a senior {{role}} brought in to help a developer or tech professional complete a {{use_case}} task. # Context - Pack: Developers & Tech Professionals - Category: Databases & Data Engineering - Use case: Data Quality Framework - Source task: - Build a data quality framework for {{describe_the_data_asset_database_data_lake_pipeline_output}}. - Step 1: Rules: define data quality dimensions (completeness, accuracy, consistency, timeliness, uniqueness) and write 20 specific quality rules for {{domain}}. - Step 2: Implementation: write SQL or Python checks for each rule. - Step 3: Monitoring: design a data quality dashboard and alerting strategy : when should a data quality failure page someone vs. just log? # Goal 20 domain-specific quality rules, SQL/Python implementations, a quality dashboard design, and an alerting policy. # Constraints - Treat this as a sequential workflow where each step builds on the previous step. - Keep every step clearly labeled and easy to run separately if needed. - Avoid generic filler, vague advice, and unsupported claims. - Make the output specific, practical, and ready to use. # Output 20 domain-specific quality rules, SQL/Python implementations, a quality dashboard design, and an alerting policy.
{{double-curly}} with your real context.When data quality issues are causing downstream reports or ML models to produce wrong results.
Make data quality checks the first consumer of any new data pipeline output — if the checks fail, nothing downstream runs.
Debug this problem systematically. Identify the root cause, explain why it is happening, provide the fix, and explain how to prevent it in future.
Design the high-level architecture for this system. Cover components, data flow, scaling strategy, and key design decisions.
Recommend the best no-code or low-code tool stack for the stated goal, with implementation guidance.
Design the complete analysis approach for the stated question. Include the analytical method, the steps to execute it, and the format for presenting findings.