Category: Learning Levels

From December 1st to December 5th, Amazon Web Services (AWS) will hold its annual premier learning event: re:Invent. There are over 2000+ learning sessions that focus on specific topics at various skill levels, and the compute team have created 76 unique sessions for you to choose. There are many sessions you can choose from, and we are here to help you choose the sessions that best fit your needs. Even if you cannot join in person, you can catch-up with many of the sessions on-demand and even watch the keynote and innovation sessions live.

This post examines the benefits of transitioning Lambda functions to IPv6, provides practical guidance for implementing dual-stack support in your Lambda environment, and considerations for maintaining compatibility with existing systems during migration.

In this post, you’ll learn how to use AWS Step Functions Distributed Map to process Amazon Athena data manifest and Parquet files through a step-by-step demonstration.

In this post, we explore how to optimize processing array data embedded within complex JSON structures using AWS Step Functions Distributed Map. You’ll learn how to use ItemsPointer to reduce the complexity of your state machine definitions, create more flexible workflow designs, and streamline your data processing pipelines—all without writing additional transformation code or AWS Lambda functions.

In this post, you’ll learn how to process Amazon S3 objects at scale with the new AWS Step Functions Distributed Map S3 prefix and transformation capabilities.

You can use AWS Step Functions to orchestrate complex business problems. However, as workflows grow and evolve, you can find yourself grappling with monolithic state machines that become increasingly difficult to maintain and update. In this post, we show you strategies for decomposing large Step Functions workflows into modular, maintainable components.

In this post, you learn how to implement blue/green deployments by using Amazon API Gateway for your APIs. For this post, we use AWS Lambda functions on the backend. However, you can follow the same strategy for other backend implementations of the APIs. All the required infrastructure is deployed by using AWS Serverless Application Model (AWS SAM).

When migrating Amazon EC2 instances with encrypted Amazon EBS volumes across AWS Regions with in the same account or a different account, you face a particular challenge: AWS KMS keys are AWS Region-specific and cannot be shared across AWS Regions. This post provides a step-by-step approach to successfully migrate your encrypted EC2 instances without compromising your security posture by sharing your KMS keys.

This post builds on our workshop “Build multi-party crypto wallets with AWS Nitro Enclaves” which demonstrates a Shamir Secret Sharing (SSS) application. The SSS app securely splits cryptographic private keys into multiple shards, requiring a threshold number to reconstruct the original key, ideal for Nitro Enclaves as it prevents any single party from accessing the complete key while maintaining operational functionality. AWS Nitro Enclaves provide isolated environments that keep critical operations such as decryption and cryptographic key management secure from both from root user and external threats.

Users usually package their function code as container images when using machine learning (ML) models that are larger than 250 MB, which is the Lambda deployment package size limit for zip files. In this post, we demonstrate an approach that downloads ML models directly from Amazon S3 into your function’s memory so that you can continue packaging your function code using zip files.