Cloud pods are persistent state snapshots of your LocalStack instance that can easily be stored, versioned, shared, and restored. Cloud Pods can be used for various purposes, such as:• Save and manage snapshots of active LocalStack instances.• Share state snapshots with your team to debug collectively.• Automate your testing pipelines by pre-seeding CI environments.• Create reproducible development and testing environments locally.In this session from LocalStack Community Meetup July '24, Bart Szydlowski explores how to use Cloud Pods to accelerate your cloud development & testing. He showcases how you can get started with Cloud Pods, integrate them into your testing pipelines, and make it easy for your team members to be onboarded to your cloud infrastructure — running all on your local machine!Docs: https://docs.localstack.cloud/user-guide/state-management/cloud-pods/

Why wait for the cloud to test your app? In this episode, we’ll write and run an integration test to validate our LocalStack app. You’ll learn how to upload a file, trigger the Lambda-SQS-DynamoDB flow, and assert the results all locally.

Testing AWS CI/CD pipelines in the cloud can be slow, error-prone, and hard to debug, especially when you're wrestling with IAM permissions or waiting on long feedback cycles. This session walks through how you can now emulate complete DevOps workflows locally using LocalStack.We cover recent additions to LocalStack that support new service providers such as: CodeBuild: Run build processes across different runtimes directly on your machine CodeDeploy: Emulate deployment steps without touching the real infrastructure CodePipeline: Create and test CI pipelines, transitions, and triggers locallyThrough a live demo, we’ll walk through a working example of a CI/CD pipeline — building a Rust project, deploying it, and running the pipeline stages — all without leaving your laptop.This session is useful for developers building or debugging AWS-native CI/CD workflows and looking for faster, more controlled ways to test them.

dbt (Data Build Tool) helps data engineers manage data transformations using modular SQL and brings version control, testing, and documentation to their transformation logic. However, running dbt against production data warehouses like Snowflake can be slow, expensive, and risky.This session introduces a new way to develop and test dbt workflows locally using the Snowflake emulator in LocalStack. You'll learn how to: Set up a local dbt environment Configure dbt to connect to the Snowflake emulator Run and validate dbt models locally without using a real Snowflake account Iterate quickly on transformations before pushing them to productionThrough a hands-on factory app example, we’ll walk through how to use the Snowflake emulator to run dbt models on your laptop, helping you test logic, catch issues early, and reduce cloud costs.