Change Data Capture

In this lesson you will learn change data capture in DynamoDB, why it matters within advanced, and how to use it correctly with clear, copy-ready examples.

Change Data Capture Overview

Change Data Capture lets you structure DynamoDB work so it stays readable, testable, and easy to scale. Instead of ad-hoc code, you follow a clear pattern that other developers can recognise immediately.

The key is to keep change data capture focused and predictable. Start from the minimal example here, then layer in only the complexity your feature actually needs.

import { DynamoDBClient } from '@aws-sdk/client-dynamodb';
import { DynamoDBDocumentClient, GetCommand, PutCommand } from '@aws-sdk/lib-dynamodb';

const client = new DynamoDBClient({});
const docClient = DynamoDBDocumentClient.from(client);

// reuse docClient across the module for efficient, typed access
await docClient.send(new PutCommand({ TableName: 'Orders', Item: { pk: '1' } }));

The DynamoDBDocumentClient maps plain JavaScript objects to DynamoDB item format for you.

Change Data Capture Example

import { DynamoDBClient } from '@aws-sdk/client-dynamodb';
import { DynamoDBDocumentClient } from '@aws-sdk/lib-dynamodb';

const docClient = DynamoDBDocumentClient.from(new DynamoDBClient({}));
// docClient.send(new PutCommand(...)) etc.

Start from a minimal Change Data Capture example and grow it only as needed.
Keep configuration explicit so Change Data Capture behaves the same in every environment.
Name things clearly so teammates understand your Change Data Capture at a glance.
Add tests around Change Data Capture early to lock in expected behaviour.

Amazon DynamoDB Cheatsheet

Handy DynamoDB (AWS SDK v3) reference related to change data capture.

Operation	Command	Purpose
Create/replace	`PutCommand`	Write an item
Read one	`GetCommand`	Fetch by primary key
Update	`UpdateCommand`	Modify attributes
Delete	`DeleteCommand`	Remove an item
Query	`QueryCommand`	Efficient key-based read
Scan	`ScanCommand`	Full-table read (avoid)
Transaction	`TransactWriteCommand`	Atomic multi-item writes

How Change Data Capture Works in DynamoDB

Change Data Capture builds on DynamoDB's key-value and document model, where every item lives in a partition chosen by its partition key and is optionally ordered by a sort key.

The DynamoDBDocumentClient maps plain JavaScript objects to DynamoDB item format for you.

Design access patterns first, then model keys around them.
Prefer Query over Scan for predictable performance.
Use expressions to read and write only what you need.
Keep items small and avoid hot partitions.

Practical Guidance for Change Data Capture

In production, change data capture should be cost-aware and resilient. Right-size capacity, handle throttling with retries, and lean on indexes to support your query patterns.

Concern	Recommendation
Performance	Query by key; avoid table scans
Cost	Use on-demand or right-sized provisioned capacity
Modeling	Design for known access patterns
Reliability	Retry throttled requests with backoff

Common Mistakes

Copying change data capture snippets without understanding what each line does.
Skipping error handling and edge cases when wiring up change data capture.
Leaving change data capture untested, so regressions slip into production.
Over-engineering change data capture before you actually need the extra flexibility.

Key Takeaways

Change Data Capture is a core part of working effectively with DynamoDB.
Start small and keep change data capture focused on a single responsibility.
Apply consistent patterns so change data capture scales across your project.
Test and document change data capture to keep it maintainable over time.

Pro Tip

When you get stuck on change data capture, reduce it to the smallest reproducible example first — most DynamoDB issues become obvious once the noise is gone.