Key points

• Kinesis Data Streams retains ordered records by shard and lets consumers maintain independent progress.
• Partition-key distribution, shard throughput, retention, replay, and consumer mode are central design choices.

Best-known use cases

• Ingest ordered clickstream, telemetry, or transaction events in real time.
• Let multiple consumers process and replay the same event stream independently.

What candidates often confuse it with

• Kinesis Data Streams provides AWS stream primitives; MSK is the Kafka-compatible alternative and Firehose is managed delivery.

Key takeaway

Choose Kinesis Data Streams for replayable real-time records that need ordering and independent consumers.

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).
• 1.1.7 — Call a Lambda function from Kinesis.
• 1.1.9 — Implement throttling and overcoming rate limits (for example, DynamoDB, Amazon RDS, Kinesis).
• 1.1.10 — Manage fan-in and fan-out for streaming data distribution.
• 1.1.11 — Describe replayability of data ingestion pipelines.
• 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]).

Learn more

• DEA-C01 in-scope services
• DEA-C01 Domain 1: Data Ingestion and Transformation
• DEA-C01 Domain 2: Data Store Management