A developer is working on a serverless application that needs to process any changes to an Amazon DynamoDB table with an AWS Lambda function. How should the developer configure the Lambda function to detect changes to the DynamoDB table?
A.
Create an Amazon Kinesis data stream, and attach it to the DynamoDB table. Create a trigger to connect the data stream to the Lambda function.
B.
Create an Amazon EventBridge rule to invoke the Lambda function on a regular schedule. Conned to the DynamoDB table from the Lambda function to detect changes.
C.
Enable DynamoDB Streams on the table. Create a trigger to connect the DynamoDB stream to the Lambda function.
D.
Create an Amazon Kinesis Data Firehose delivery stream, and attach it to the DynamoDB table. Configure the delivery stream destination as the Lambda function.
B) Eliminated - Polling the table for changes introduces unnecessary complexity and delay
C) Correct - DynamoDB Streams is specifically designed for this use case, capturing item-level changes in the table.
D) Eliminated - Firehose is used for data delivery and transformation to destinations like S3 or Redshift, not for processing DynamoDB table changes.
While option A is technically possible, it introduces unnecessary complexity for detecting changes in a DynamoDB table. Using DynamoDB Streams directly is a more streamlined and optimized solution for this use case.
C
Enabling DynamoDB Streams on the table allows you to capture and process changes (inserts, updates, deletes) to the table in real-time. You can then create a Lambda trigger that listens to the DynamoDB stream and invokes the Lambda function whenever there is a change in the table. This is a common and effective way to react to changes in DynamoDB tables with AWS Lambda functions.
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