What is AWS data engineering services?

AWS provides a comprehensive ecosystem for data engineering and analytics: Ingestion: Kinesis Data Streams (real-time streaming, custom consumers, retain 24h-365 days); Kinesis Data Firehose (load to S3/Redshift/Elasticsearch, automatic transformations, no consumer management); Database Migration Service (DMS — migrate databases, ongoing replication, CDC); AWS Glue (ETL service — discover schema, transform, catalog data); MSK (Managed Kafka — fully managed Apache Kafka). Storage: S3 (data lake — Parquet, ORC, Avro, CSV); Redshift (petabyte-scale data warehouse, columnar storage, Redshift Spectrum for S3 queries); EMR (managed Hadoop/Spark/Hive/Presto — big data processing); AWS Lake Formation (govern and secure data lake). Processing: AWS Glue (Spark ETL, Python/Scala, serverless, Glue Data Catalog); EMR (Spark, Hive, Hadoop, Flink on managed cluster); Lambda (small transformations, event-driven); AWS Batch (batch processing jobs, managed compute). Analytics/Query: Athena (serverless SQL on S3 — pay per query, uses Data Catalog schemas); Redshift (data warehouse SQL); QuickSight (BI dashboarding, SPICE in-memory engine); OpenSearch Service (Elasticsearch compatible — search and log analytics). ML: SageMaker (managed ML platform — train, deploy, monitor models); SageMaker Feature Store, Pipeline, Studio. Orchestration: MWAA (Managed Airflow), Glue Workflows, Step Functions. Data lake pattern: Kinesis/DMS → S3 raw → Glue ETL → S3 refined → Athena/Redshift for analysis → QuickSight for visualization.