Stream processing patterns in DynamoDB - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a DynamoDB stream?

A DynamoDB stream captures a time-ordered sequence of item-level changes in a DynamoDB table. It allows applications to react to data changes in real time.

beginner

Explain the 'Event-driven processing' pattern in DynamoDB streams.

This pattern uses DynamoDB streams to trigger actions immediately after data changes. For example, a Lambda function can process new or updated items as soon as they appear in the stream.

intermediate

What is the 'Change data capture' (CDC) pattern?

CDC captures and records changes made to data in DynamoDB tables. It enables replicating changes to other systems or analytics tools by reading the stream of changes.

intermediate

Describe the 'Materialized view' pattern using DynamoDB streams.

This pattern keeps a separate table updated in real time to reflect a transformed or aggregated view of the original data. Streams trigger updates to the materialized view whenever source data changes.

advanced

Why is ordering important in DynamoDB stream processing?

Ordering ensures that changes are processed in the exact sequence they occurred. This prevents data inconsistencies and ensures correct application behavior when reacting to updates.

What does a DynamoDB stream capture?

A Backup snapshots

B User login events

C Table schema changes

D Item-level changes in a table

DynamoDB streams capture item-level changes such as inserts, updates, and deletes.

Which AWS service is commonly used to process DynamoDB stream events?

A AWS Lambda

B Amazon S3

C Amazon EC2

D AWS CloudTrail

AWS Lambda can be triggered by DynamoDB streams to process changes automatically.

In the 'Materialized view' pattern, what is updated in real time?

A A separate table reflecting transformed data

B The original table schema

C User permissions

D Backup files

A materialized view is a separate table updated to reflect transformed or aggregated data.

Why is processing order important in stream processing?

A To encrypt data

B To reduce storage costs

C To maintain data consistency

D To speed up queries

Processing changes in order prevents inconsistencies and ensures correct results.

What does the Change Data Capture (CDC) pattern enable?

A Changing table indexes

B Replicating data changes to other systems

C Deleting old data automatically

D Encrypting data at rest

CDC captures changes so they can be replicated or analyzed elsewhere.

Describe three common stream processing patterns used with DynamoDB streams and their purposes.

Think about how streams help react to or replicate data changes.

Explain why maintaining the order of events is important when processing DynamoDB streams.

Consider what happens if updates arrive out of order.