Scenario: Your application receives a sudden surge of incoming messages through SNS. How can you ensure that the AWS Lambda functions triggered by these messages can handle the increased load efficiently?

Setting concurrency limits helps manage the number of concurrent executions, preventing Lambda functions from being overwhelmed.

Scenario: You need to implement a serverless architecture where incoming data from IoT devices triggers AWS Lambda functions for processing. How would you design the integration between SNS and AWS Lambda in this scenario?

Publishing data to an SNS topic allows SNS to trigger AWS Lambda functions for processing the incoming data from IoT devices.

DynamoDB Streams captures data modification events in a DynamoDB table, allowing you to track changes and trigger actions based on those changes.

A DynamoDB stream ARN uniquely identifies a specific stream.

Using partition keys ensures that records with the same partition key are processed in order.

Real-time analytics, such as processing and analyzing data changes in real-time, is a key use case for integrating DynamoDB Streams with AWS Lambda.

DynamoDB Streams uses sequence numbers to maintain the correct order of records, ensuring data consistency across multiple shards.

The limited read throughput of DynamoDB Streams can impact scalability and performance, particularly when processing high volumes of data.

DynamoDB Streams are triggered by changes to DynamoDB tables, capturing data modifications and enabling subsequent processing.

Change data capture is the process of capturing a time-ordered sequence of item-level modifications in a DynamoDB table.