https://docs.aws.amazon.com/codedeploy/latest/userguide/deployment-steps.html

The latency in processing and ingesting logs can be caused by several factors, such as the throughput of the Kinesis data stream, the concurrency of the Lambda function, and the configuration of the event source mapping. To reduce the latency, the following steps can be taken:
Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer. This will allow the Lambda function to receive records from the data stream with dedicated throughput of up to 2 MB per second per shard, independent of other consumers1. This will reduce the contention and delay in accessing the data stream.
Increase the ParallelizationFactor setting in the Lambda event source mapping. This will allow the Lambda service to invoke more instances of the function concurrently to process the records from the data stream2. This will increase the processing capacity and reduce the backlog of records in the data stream.
Configure reserved concurrency for the Lambda function that processes the logs. This will ensure that the function has enough concurrency available to handle the increased load from the data stream3. This will prevent the function from being throttled by the account-level concurrency limit.
The other options are not effective or may have negative impacts on the latency. Option D is not suitable because increasing the batch size in the Kinesis data stream will increase the amount of data that the Lambda function has to process in each invocation, which may increase the execution time and latency4. Option E is not advisable because turning off the ReportBatchItemFailures setting in the Lambda event source mapping will prevent the Lambda service from retrying the failed records, which may result in data loss. Option F is not necessary because increasing the number of shards in the Kinesis data stream will increase the throughput of the data stream, but it will not affect the processing speed of the Lambda function, which is the bottleneck in this scenario.
Reference:
1: Using AWS Lambda with Amazon Kinesis Data Streams - AWS Lambda
2: AWS Lambda event source mappings - AWS Lambda
3: Managing concurrency for a Lambda function - AWS Lambda
4: AWS Lambda function scaling - AWS Lambda
5: AWS Lambda event source mappings - AWS Lambda
6: Scaling Amazon Kinesis Data Streams with AWS CloudFormation - Amazon Kinesis Data Streams

https://aws.amazon.com/es/blogs/devops/complete-ci-cd-with-aws-codecommit-aws-codebuild-aws-codedeploy-and-aws-codepipeline/

Step 1: Reacting to CloudFormation Stack Events with EventBridge
AWS CloudFormation emits events during the lifecycle of a stack, including the UPDATE_COMPLETE event after a successful stack update. You can use Amazon EventBridge to detect this event and trigger a specific action (such as invoking a Lambda function).
Action: Create an EventBridge rule that listens for the UPDATE_COMPLETE event for the CloudFormation stack.
Why: EventBridge allows you to automatically detect CloudFormation stack lifecycle events and take action based on them.
Step 2: Invoking the Lambda Function
After the EventBridge rule detects the UPDATE_COMPLETE event, it can invoke the pre-configured Lambda function to apply or update the tag on the specific CloudFormation stack instance.
Action: Configure the EventBridge rule to invoke the Lambda function when the UPDATE_COMPLETE event occurs.
Why: This automation ensures that the tag is applied immediately after a successful stack update without any manual intervention.
Reference:
This corresponds to Option C: Create an Amazon EventBridge rule that matches the UPDATE_COMPLETE event pattern for the instance status of the CloudFormation stack. Configure the rule to invoke the Lambda function.