Creating a unified logging solution for multi-cloud environments using AWS Lambda, Amazon S3, and AWS ElasticsearchPython
Sure, I'd be happy to help. This example program creates an AWS Lambda function, deploys logs in an Amazon S3 bucket, and uses AWS Elasticsearch for log analysis.
To access resources across different AWS accounts and regions, you can use the
pulumi.stack_reference.StackReferenceclass, which allows you to export stack outputs from one stack to use as an input in another stack.
Please check the following program:
This Pulumi program creates an AWS S3 bucket to store logs (aws.s3.Bucket) and an IAM Role (aws.iam.Role). After that, it attaches the AmazonESFullAccess policy to the role for full access to AWS ElasticSearch. The role is then associated with an AWS Lambda function (aws.lambda_.Function) that is responsible for putting the logs into the S3 bucket. Lastly, an AWS Elasticsearch domain (aws.es.Domain) is created for log analysis. The bucket name, lambda function ARN, and Elasticsearch domain endpoint are exported as stack outputs.
This provides a foundation for a logging solution across multiple environments. Note that the cross-account and cross-region access, specific logging event triggers, and log analysis are implementation details that you would need to handle based on your requirements (not shown in the example).
// Put your code herepart with your actual Lambda function code. It could make use of the
LOG_BUCKET_NAMEenvironment variable to access the S3 bucket for storing logs.