##### Question Design an end-to-end advertising clickstream ingestion and analytics platform that ingests events through Kafka, stores raw and curated data in S3, and supports interactive queries with Presto. Cover the following: 1. **Ingestion (Kafka):** Define the topic partitioning strategy, message schema and serialization (with a schema registry), partition keys, ordering and delivery semantics, and consumer groups for multiple use cases (e.g., real-time CTR metrics within one minute *and* batch ETL to S3). 2. **Storage (S3 data lake):** Define the S3 layout and partitioning strategy for efficient Presto queries (e.g., by date / hour / campaign), the raw vs. curated zone separation, catalog and schema management, file formats, and compaction. 3. **Query engine (Presto):** Describe catalog/metastore integration, partition pruning, summary/materialized tables, and query optimizations. 4. **Scale & resilience:** Scale the system to 1M+ events/second. Address backpressure handling, broker outages, consumer restarts, exactly-once vs. at-least-once trade-offs, failover and recovery, data reprocessing/backfills, and late/out-of-order events. 5. **Governance & cost:** Cover schema evolution, PII governance, and cost optimization. 6. **Trade-offs:** Compare real-time vs. batch processing and explain where each is appropriate.