← Microsoft Interview Insights
I jumped straight into the ingestion pipeline without asking enough about scale, frequency, or schema consistency across the buckets.
Start by clarifying requirements (volume, latency, schema variability, processing needs) and then propose a scalable, event-driven architecture using S3 event notifications to trigger processing. Discuss trade-offs between batch and stream processing, and emphasize fault tolerance, scalability, and cost-efficiency.
Pro tip: Demonstrate awareness of real-world constraints like S3 eventual consistency, throttling limits, and cost implications of frequent LIST operations; propose using S3 Inventory for batch discovery and SQS for event buffering to handle bursts.
Ask about data volume, velocity, schema consistency, latency requirements, and downstream processing needs to tailor the design.
Use S3 event notifications to trigger processing, but buffer events with SQS to handle spikes and avoid Lambda throttling; for batch, use S3 Inventory or AWS Glue crawlers.
Select between serverless (Lambda, Fargate) for variable loads or managed big data (EMR, Glue) for heavy transformations; consider stream processing (Kinesis, Kafka) if near-real-time is needed.
Define schema handling (schema-on-read vs. schema-on-write), partition data for efficient querying, and choose storage (S3, data lake, warehouse) based on access patterns.
Implement idempotent processing, dead-letter queues, retries, and monitoring; design for horizontal scaling and cost optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.