Purpose: To assess your ability to design scalable, reliable, and efficient data infrastructure.
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Understand Requirements
- Clarify functional requirements (e.g., data ingestion, processing, querying).
- Identify non-functional requirements (e.g., scalability, latency, throughput, fault tolerance).
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Back-of-the-Envelope Estimations
- Estimate data volume, throughput, query patterns, and peak loads.
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Define Data Flow and APIs
- Design data pipelines: Ingestion → Processing → Storage → Consumption.
- Specify APIs for data ingestion and query execution.
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High-Level Architecture
- Choose key components: Data sources, ETL tools, storage (e.g., data lake vs. data warehouse), query engines, caching layers.
- Define batch vs. streaming approaches, if applicable.
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Component Deep Dive
- Discuss storage technologies (e.g., Parquet vs. ORC, columnar vs. row storage).
- Design query execution paths, indexing strategies, and data partitioning.
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Address Scalability and Reliability
- Implement data replication, sharding, and high-availability patterns.
- Discuss monitoring, alerting, and disaster recovery strategies.