Design Data Quality and Observability Pipeline
Design a Production Data Quality and Observability Pipeline
Design a comprehensive production framework for data orchestration, data quality, and observability, capable of handling both batch and streaming data pipelines.
Your design must explicitly address the following:
- Data Orchestration: Detail how you would orchestrate data pipelines, specifically considering upstream and downstream dependencies.
- Data Quality Checks: Describe the types of data quality checks you would implement. Provide examples such as threshold checks, upstream dependency checks, schema checks, null checks, and primary-key checks.
- Data Observability Framework: Outline the key components and functionalities that a robust data observability framework would include.
- Streaming Data Validation Failure: If a small percentage of streaming records (e.g., 2%) fails validation, explain your decision process and handling strategy regarding whether to stop the entire pipeline or implement an alternative approach.
AI interview prep
Core dilemma
Balancing the need for high data quality and reliability across diverse data processing paradigms (batch and streaming) with the operational complexity, performance, and cost of implementing a robust, fault-tolerant, and observable data platform.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design Data Quality and Observability Pipeline
Design a Production Data Quality and Observability Pipeline
Design a comprehensive production framework for data orchestration, data quality, and observability, capable of handling both batch and streaming data pipelines.
Your design must explicitly address the following:
- Data Orchestration: Detail how you would orchestrate data pipelines, specifically considering upstream and downstream dependencies.
- Data Quality Checks: Describe the types of data quality checks you would implement. Provide examples such as threshold checks, upstream dependency checks, schema checks, null checks, and primary-key checks.
- Data Observability Framework: Outline the key components and functionalities that a robust data observability framework would include.
- Streaming Data Validation Failure: If a small percentage of streaming records (e.g., 2%) fails validation, explain your decision process and handling strategy regarding whether to stop the entire pipeline or implement an alternative approach.