Database
CoreAmazon DynamoDB
Managed key-value and document data with streams, throttling controls, and TTL.
Key points
- DynamoDB serves modeled key-value and document access with partition and sort keys chosen from request patterns.
- Streams, throttling, hot-key avoidance, and asynchronous TTL expiration are separate operational concerns.
Best-known use cases
- Serve low-latency key-value records using known partition-key access patterns.
- Capture item changes through DynamoDB Streams for downstream processing.
- Expire temporary pipeline state automatically with time to live.
What candidates often confuse it with
- DynamoDB is a key-access store; RDS or Aurora serves relational transactions and MemoryDB serves durable in-memory access.
Key takeaway
Choose DynamoDB when predictable key-based access and managed scale matter more than relational joins.
Relevant exam tasks
- D1.1 — Task 1.1: Perform data ingestion
- 1.1.1 — Read data from streaming sources (for example, Amazon Kinesis, Amazon Managed Streaming for Apache Kafka [Amazon MSK], Amazon DynamoDB Streams, AWS DMS, AWS Glue, Amazon Redshift).
- D1.4 — Task 1.4: Apply programming concepts
- 1.4.6 — Use AWS SAM to package and deploy serverless data pipelines (for example, Lambda functions, Step Functions, DynamoDB tables).
- D2.1 — Task 2.1: Choose a data store
- 2.1.1 — Implement the appropriate storage services for specific cost and performance requirements (for example, Amazon Redshift, Amazon EMR, AWS Lake Formation, Amazon RDS, Amazon DynamoDB, Amazon Kinesis Data Streams, Amazon Managed Streaming for Apache Kafka [Amazon MSK]).
- D2.3 — Task 2.3: Manage the lifecycle of data
- 2.3.4 — Manage S3 versioning and DynamoDB TTL.
- D2.4 — Task 2.4: Design data models and schema evolution
- 2.4.1 — Design schemas for Amazon Redshift, DynamoDB, and Lake Formation.