In a serverless architecture where DynamoDB updates trigger Lambda functions that write to another DynamoDB table, what is the best approach to maintain data consistency?

hard🚀 Application Q8 of 15

DynamoDB - with Serverless

AWrite to both tables simultaneously in the client application

BInvoke Lambda functions directly from the client after updating the first table

CUse scheduled Lambda functions to periodically sync both tables

DUse DynamoDB Streams with Lambda and implement idempotent writes in the Lambda function

Step-by-Step Solution

Solution:

Step 1: Use DynamoDB Streams to trigger Lambda
This ensures Lambda reacts to actual data changes reliably.
Step 2: Implement idempotency in Lambda
Idempotent writes prevent duplicate or inconsistent data during retries.
Step 3: Avoid client-side direct invocation or scheduled sync
These approaches risk inconsistency and latency.
Final Answer:
Use DynamoDB Streams with Lambda and implement idempotent writes in the Lambda function -> Option D
Quick Check:
Streams + idempotent Lambda ensures consistency [OK]

Quick Trick: Use Streams and idempotent Lambda writes [OK]

Common Mistakes:

MISTAKES

Triggering Lambda from client instead of Streams
Relying on scheduled syncs causing delays
Writing to both tables from client risking partial failure

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In a serverless architecture where DynamoDB updates trigger Lambda functions that write to another DynamoDB table, what is the best approach to maintain data consistency?

Step 1: Use DynamoDB Streams to trigger Lambda

Step 2: Implement idempotency in Lambda

Step 3: Avoid client-side direct invocation or scheduled sync

Final Answer:

Quick Check:

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