🗂️ AWS Database & Analytics: Quick Review
AWS has a database or analytics service for nearly every workload. The exam skill isn't memorizing every feature — it's recognizing the scenario and matching it to the right service.
🎯 Exam Tip
✅ Match the use case to the service
✅ No deep DB admin knowledge needed
✅ Most questions describe a scenario and ask "which service fits?"
🧠 Service-by-Service Recap
🗄️ RDS & Aurora
Use case: Relational databases, OLTP — banking, e-commerce, customer management
Key points: SQL-based, built for everyday transactions
Also know: Multi-AZ, Read Replicas, Multi-Region — and when to use each
⚡ ElastiCache
Use case: In-memory caching — speeding up apps, reducing DB load, serving hot data fast
In-memory cache → ElastiCache
🔑 DynamoDB
Use case: Serverless NoSQL key-value database
Key points: Fully managed, highly scalable, low latency
Key-value NoSQL → DynamoDB
🚀 DAX
Use case: In-memory cache built specifically for DynamoDB
DynamoDB caching → DAX
📊 Redshift
Use case: Data warehousing, OLAP — analytics, BI, reporting
Key points: SQL-based, columnar storage, optimized for analysis
Data warehouse / analytics → Redshift
📈 EMR
Use case: Big data processing via Hadoop clusters
Key points: Supports Spark and Hadoop, used for large-scale analysis
Hadoop cluster → EMR
🔍 Athena
Use case: Serverless SQL queries directly on S3 data
Key points: No need to load data into a database first
SQL + S3 → Athena
📉 QuickSight
Use case: Business Intelligence and dashboards — charts, graphs, interactive reports
Key points: Serverless, connects to many AWS data sources
Dashboards / BI → QuickSight
📄 DocumentDB
Use case: MongoDB-compatible NoSQL document database
Key points: Stores JSON documents, fully managed
MongoDB → DocumentDB
🔗 Managed Blockchain
Use case: Managed blockchain networks
Key points: Supports Hyperledger Fabric and Ethereum
Blockchain → Managed Blockchain
🔄 Glue
Use case: ETL — data prep, transformation, pipelines
Key points: Includes the Glue Data Catalog
ETL → Glue
🚚 DMS
Use case: Database migration — on-prem to AWS, engine upgrades, conversions
Key points: Supports homogeneous and heterogeneous migrations
Database migration → DMS
🌐 Neptune
Use case: Graph database
Examples: Social networks, recommendation engines, fraud detection, knowledge graphs
Graph database → Neptune
⏱️ Timestream
Use case: Time series database
Examples: IoT sensor data, app monitoring, performance metrics, financial data
Time series data → Timestream
📋 Cheat Sheet
Service | Primary Use Case | Exam Keywords |
|---|---|---|
RDS | Relational database (OLTP) | SQL, Transactions |
Aurora | Cloud-native relational database | MySQL, PostgreSQL |
ElastiCache | In-memory cache | Redis, Memcached, Caching |
DynamoDB | Serverless NoSQL database | Key-value, NoSQL |
DAX | DynamoDB cache | DynamoDB Accelerator |
Redshift | Data warehouse (OLAP) | Analytics, Reporting |
EMR | Big data processing | Hadoop, Spark |
Athena | Serverless SQL on S3 | SQL, S3 |
QuickSight | Business Intelligence | Dashboards, Charts |
DocumentDB | MongoDB-compatible database | JSON, MongoDB |
Managed Blockchain | Blockchain service | Ethereum, Hyperledger Fabric |
Glue | ETL | Extract, Transform, Load |
DMS | Database migration | Migration |
Neptune | Graph database | Relationships, Social Network |
Timestream | Time series database | IoT, Metrics, Monitoring |
📝 Summary
The core exam skill: match the use case to the service.
🗄️ Relational database → RDS / Aurora
⚡ In-memory cache → ElastiCache
🔑 Serverless NoSQL → DynamoDB
🚀 DynamoDB cache → DAX
📊 Data warehouse / OLAP → Redshift
📈 Big data / Hadoop → EMR
🔍 Serverless SQL on S3 → Athena
📉 BI & dashboards → QuickSight
📄 MongoDB-compatible → DocumentDB
🔗 Blockchain → Managed Blockchain
🔄 ETL → Glue
🚚 Database migration → DMS
🌐 Graph database → Neptune
⏱️ Time series database → Timestream
Lock in these associations and you're ready for database and analytics questions on the exam.