AWS Data Lake ETL Pipeline ingests IoT events via Kinesis, Kafka topics via MSK, and batch extracts from on-premises databases, routing them through Glue Streaming and Batch ETL jobs into S3 zones (Raw, Curated, Aggregated). EMR Spark transforms curated data while Redshift serves analytics queries, with Athena enabling ad-hoc exploration and QuickSight powering dashboards. Lake Formation governs access across the data lake, CloudWatch monitors pipeline health, and Glue Data Catalog maintains metadata for all assets. This architecture demonstrates a modern medallion pattern combining real-time and batch ingestion with centralized governance and multi-query serving. Fork this diagram on Diagrams.so to customize ingestion sources, add additional transformation stages, or adjust storage zones for your organization's data maturity model.