AdvancedTechnical
5 min
Scalable ETL Pipeline Design
ETLData EngineeringScalability
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Interview Question
How would you design a scalable ETL pipeline for processing terabytes of data daily with low latency?
Key Points to Cover
- Design data ingestion with Kafka or Kinesis
- Apply distributed processing frameworks (Spark, Flink)
- Ensure schema evolution handling and data validation
- Optimize storage formats (Parquet, ORC) and partitioning
Evaluation Rubric
Defines scalable ingestion strategy30% weight
Explains distributed processing design30% weight
Optimizes storage and query efficiency20% weight
Handles schema evolution and validation20% weight
Hints
- 💡Think streaming vs batch trade-offs.
Common Pitfalls to Avoid
- ⚠️Over-reliance on a single processing technology without considering alternatives for different use cases.
- ⚠️Underestimating the complexity of schema evolution and not implementing a robust management strategy.
- ⚠️Neglecting data validation early in the pipeline, leading to downstream data quality issues.
- ⚠️Insufficient monitoring and alerting, causing issues to go unnoticed for extended periods.
- ⚠️Choosing a storage solution that doesn't align with read latency or query patterns, creating bottlenecks.
Potential Follow-up Questions
- ❓How would you monitor ETL latency?
- ❓What about handling corrupt records?
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