Design a data pipeline to ingest, clean, and segment user behavior data for a multi‑channel marketing funnel. What architecture would you choose and why?

Framework + step‑by‑step strategy (120‑150 words, no story)

I would architect a hybrid ingestion system that balances batch and streaming to meet latency and volume requirements. First, I’d set up a Kafka cluster to capture real‑time clickstream events, ensuring low‑latency ingestion for time‑sensitive attribution. Next, I’d use Spark Structured Streaming to transform and enrich data on the fly, writing results to a Delta Lake for ACID compliance. For historical data, a nightly batch ETL would consolidate logs into a Snowflake data warehouse, enabling complex analytical queries. Schema evolution would be managed via Confluent Schema Registry, and all data flows would be instrumented with Prometheus metrics and Grafana dashboards for real‑time monitoring. Finally, I’d implement automated data quality checks using Great Expectations, triggering alerts when thresholds are breached. This architecture ensures scalability, data integrity, and actionable insights for growth marketing initiatives.