#	Best Practice	Description
1	Single Responsibility Principle	Each Lambda function should have a single responsibility. This makes the code easier to understand, test, and manage.
2	Statelessness	Lambda functions should be stateless. Any state needed should be passed in via the event or fetched from a persistent store like DynamoDB.
3	Idempotency	Ensure that your Lambda functions are idempotent, especially if they are triggered by events that can be delivered more than once.
4	Error Handling	Implement robust error handling to catch and handle exceptions. Ensure that errors are logged for debugging purposes.
5	Logging and Monitoring	Utilize AWS CloudWatch Logs for logging and AWS CloudWatch Metrics for monitoring your Lambda functions. Include meaningful log statements and error messages.
6	Use Environment Variables	Use environment variables for configuration settings that might change between environments.
7	Security	Adhere to the principle of least privilege. Assign only necessary permissions to your Lambda function's execution role.
8	Optimize Memory and Timeout Settings	Choose the right memory and timeout settings for your function to ensure good performance and to control costs.
9	Deployment Automation	Utilize tools like AWS SAM or Serverless Framework for deployment automation and infrastructure as code.
10	Keep Cold Start Time in Mind	Structure your code to minimize cold start times, such as by minimizing the size of your deployment package and the number of resources initialized at startup.
11	Modular Code	Write modular code and separate concerns well, which will help in making the codebase maintainable and testable.
12	Testing	Write unit tests and integration tests to cover different scenarios and edge cases. Use mock libraries to mock AWS services during testing.
13	Code Reviews	Establish a code review process to ensure code quality, adherence to standards, and to catch potential issues before deployment.
14	Documentation	Document your code well, including the expected input and output, any environment variables, and how errors are handled.
15	Version Control	Utilize version control systems and adhere to a consistent versioning strategy for your Lambda functions.
16	Local Development and Testing	Use tools and frameworks that support local development and testing of Lambda functions, such as AWS SAM Local or LocalStack. This can help catch issues early before deploying to AWS.
17	Dependency Management	Manage dependencies effectively by including only necessary libraries and modules in your deployment package to minimize the size and reduce cold start times.
18	Use of AWS SDK	Ensure the AWS SDK is used effectively and efficiently for interacting with AWS services, and that it's kept up-to-date to take advantage of any performance improvements or bug fixes.
19	AWS X-Ray Integration	Integrate AWS X-Ray to trace and analyze the performance of your Lambda functions and the downstream AWS resources they access.
20	Dead Letter Queues (DLQ)	Configure Dead Letter Queues (DLQ) for event source mappings to handle failed processing attempts and ensure data is not lost.
21	VPC Configuration	If your Lambda function needs to access resources within a VPC, ensure that VPC settings are correctly configured, and be aware of the potential for increased cold start times.
22	Batch Processing	If your use case involves processing a large number of records, consider batch processing to optimize the performance and cost-effectiveness of your Lambda functions.
23	Use of Layers	Utilize AWS Lambda Layers to manage shared code and libraries across multiple functions, which can help reduce the size of deployment packages and allow for easier updates to shared code.
24	Resource Tagging	Implement resource tagging to organize, manage, and monitor your Lambda functions. This can also assist in cost allocation and reporting.
25	Avoid Long-Running Functions	Design your functions to complete quickly. AWS Lambda is designed for short-lived operations. If a longer processing time is required, consider other services like AWS Batch or AWS Step Functions which are designed for long-running processes and workflows.