If you have worked on an ETL pipeline, can you explain which critical pipeline you have designed and describe its architecture?

I designed a real‑time clickstream ingestion pipeline for a large e‑commerce platform. The architecture consists of four layers:

1. Ingestion – User events are published to Kafka topics. A Kafka Connect source connector streams data into a Spark Structured Streaming job.
2. Processing – The Spark job performs enrichment (joining with user profile tables), aggregation (sessionization), and data quality checks. It writes results to Delta Lake tables in a raw and curated format.
3. Storage – Delta tables provide ACID guarantees and time travel. The curated layer is partitioned by date and user segment to support fast analytical queries.
4. Orchestration & Monitoring – Airflow schedules batch jobs for daily aggregates, while the streaming job is managed by Kubernetes with Prometheus metrics. Alerts are sent to Slack on failures.

Key decisions included using Delta Live Tables for the curated layer to automate retries and schema evolution, leveraging Delta Lake’s compaction for performance, and implementing a dead‑letter queue for malformed events. This design achieved low latency, high reliability, and easy observability for downstream BI tools.