High-Quality Data Extraction (Critical Data)

Scenario: You’re tasked with extracting mission-critical data (e.g., financial transactions) where accuracy and consistency are non-negotiable.

Key factors

• Data accuracy: No tolerance for errors or duplication
• Validation requirements: Stringent (every record must be validated)
• Data freshness: Real-time or batch
• Volume: Moderate to high
• Source system impact: Must not affect source systems, especially in transactional workloads

What to discuss

• Exactly-once extraction: Use exactly-once processing in streaming contexts (Flink, Kafka Streams) or deduplication mechanisms in batch extraction to ensure no duplicate records are processed.
• Validation checks: Ensure data validation during extraction (e.g., checksums, hash functions).
• Fault tolerance and recovery: Implement fault-tolerant extraction systems to guarantee no data loss even during failures.

Trade-offs

• Complexity vs. accuracy: Exactly-once processing introduces complexity but guarantees accuracy. Justify the need based on critical business requirements.
• Data freshness vs. validation: Rigorous validation may slow down extraction. Balance the need for accuracy with performance requirements.

What interviewers want to hear

• A strong understanding of exactly-once guarantees in both streaming and batch pipelines.
• How you ensure data validation to avoid incorrect or duplicated data.
• Clear handling of failures and trade-offs between complexity and correctness.