Spent the first few minutes just trying to scope it properly because 'massive scale' could mean a lot of things.
Start by clarifying requirements: data volume, velocity, latency, and quality metrics. Then propose a streaming architecture with components for ingestion, validation, transformation, and monitoring, emphasizing trade-offs between latency, cost, and accuracy. Conclude with how you'd handle scale and failures.
Pro tip: Show awareness that perfect cleaning is impossible at scale; instead, design for graceful degradation and continuous monitoring of data quality metrics to catch drift.
Ask about data sources, volume (e.g., TB/day), velocity (events/sec), latency needs (sub-second vs. minutes), and quality definitions (missing values, outliers, schema violations).
Propose a streaming pipeline: ingestion (Kafka/Kinesis), processing (Flink/Spark Streaming), storage (data lake/warehouse), and serving layer. Mention batch vs. streaming trade-offs.
Detail specific cleaning steps: schema validation, deduplication, outlier detection, missing value imputation, and normalization. Discuss how to implement them in a distributed, stateful manner.
Explain partitioning, parallel processing, backpressure handling, exactly-once semantics, and checkpointing. Discuss auto-scaling and resource management.
Describe metrics (throughput, latency, data quality), alerting, and feedback loops to update cleaning rules. Mention A/B testing of cleaning strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.