AWS Data Services: A Mental Map
AWS has a lot of data services, and the names alone don’t tell you what each one is for. This post is a mental map — how the database, analytics, and data-movement services fit together, and how to pick between them. The mental map ┌─────────────────────────────────────────────────────────────┐ │ DATABASES (operational, low latency) │ ├─────────────────────────────────────────────────────────────┤ │ RDS relational SQL │ │ DynamoDB NoSQL key-value, serverless │ │ ElastiCache in-memory cache (Redis/Memcached) │ │ DocumentDB MongoDB-compatible document DB │ │ Neptune graph DB (nodes + edges) │ │ Timestream time-series DB │ │ Managed Blockchain Hyperledger/Ethereum │ ├─────────────────────────────────────────────────────────────┤ │ ANALYTICS (batch, big data, querying) │ ├─────────────────────────────────────────────────────────────┤ │ Redshift data warehouse (columnar SQL at scale) │ │ EMR managed Hadoop/Spark cluster │ │ Athena SQL queries directly on S3 │ │ QuickSight BI dashboards/visualization │ ├─────────────────────────────────────────────────────────────┤ │ DATA MOVEMENT │ ├─────────────────────────────────────────────────────────────┤ │ Glue managed ETL (extract/transform/load) │ │ DMS Database Migration Service │ └─────────────────────────────────────────────────────────────┘ Databases These are operational, low-latency stores — the databases an application reads and writes in real time....