In real-world applications, functions can be computationally expensive or time-consuming. To improve efficiency, we can cache and reuse results for identical inputs, a pattern known as idempotency. AWS Lambda Powertools provides an idempotency utility that returns the previously successful result when a function is called repeatedly with the same input. In this post, we will explore how to use it.

AWS X-Ray helps developers analyze and debug distributed applications by providing a holistic view of requests as they travel through the system.¹ Tracer is a wrapper around the AWS X-Ray SDK for Python that offers a simplified interface for instrumenting Lambda functions.² In this post, I will show you how to use Tracer to gain insights into your function’s execution.

Metrics are a key component of monitoring and observability, allowing you to understand the behavior of your Lambda functions and identify potential issues. In this post, we will explore how to use AWS Lambda Powertools to simplify metric collection.

Powertools for AWS Lambda is a library designed to implement best practices and boost productivity when building AWS Lambda functions. In this post, we will explore its core features, focusing on logging and validation.

Building on my previous post about deploying a Lambda function using AWS SAM, this guide demonstrates how to deploy a containerized Lambda function using Terraform. The example application queries an external API (https://api.github.com) and returns the response.

When developing Lambda functions, you may need to verify the behavior of your code in a test environment before deploying it to production. For example, your Lambda function needs to send a email to your client, but you don’t want to disturb your client while still developing the code. In this post, we’ll explore how to use the AWS Serverless Application Model (SAM) and samconfig.toml to manage different environments for your Lambda functions.

AWS Lambda, a serverless computing service provided by AWS, executes code in response to events and automatically manages the compute resources required by that code. AWS SAM (Serverless Application Model) is an open-source framework for building serverless applications and provides a simplified way of defining the AWS services needed by your serverless application. This post explores configuring AWS Lambda deployment using an AWS SAM configuration file