Ever wonder why some teams intentionally break their own systems? Welcome to the world of chaos engineering — a practice that's not just for Netflix-scale infrastructure, but for any team that wants to build resilient, reliable applications.In this session, we'll demystify chaos engineering and explain why intentionally breaking things is actually the smart move. You'll learn:What chaos engineering really is (in plain English, no buzzwords)Why waiting for production failures is a terrible strategyHow to start experimenting with controlled failure locally, before it happens in the wildReal-world examples of chaos experiments that catch bugs you'd never find in traditional testingTools and techniques to get started without blowing up your infrastructureThrough practical demos using LocalStack's cloud emulation and chaos engineering tools, we'll simulate failures like network latency, service outages, and resource exhaustion right from your laptop.If you've ever said "it worked on my machine" only to watch it crash in production, this talk is for you—let's break things intentionally so they don't break unexpectedly.

Building and debugging cloud-native applications often involves slow CI/CD pipelines, hard-to-reproduce bugs, and the need for costly shared environments. LocalStack offers a better way — letting developers simulate real AWS services entirely on their local machine.In this presentation, Kiah Imani gives a hands-on walkthrough of building and testing AWS workflows locally with LocalStack. From Lambda functions to S3 pre-signed uploads and SNS/SQS pipelines, you'll see how to prototype, debug, and iterate on cloud-native apps without ever deploying to the cloud.### Resources- S3: https://docs.localstack.cloud/aws/services/s3/- Lambda: https://docs.localstack.cloud/aws/services/lambda/- SQS: https://docs.localstack.cloud/aws/services/sqs/- SNS: https://docs.localstack.cloud/aws/services/sns/- Repo: https://github.com/localstack-samples/sample-serverless-image-resizer-s3-lambda/

Running AI/ML workloads in the cloud can be expensive, opaque, and difficult to iterate on. LocalStack changes this by enabling engineers to develop and test AI-powered cloud applications entirely locally, emulating services like SageMaker, Bedrock, Redshift, and Snowflake.In this presentation, Waldemar Hummer, CTO of LocalStack, demonstrates how to prototype and validate AI & ML data pipelines safely and cost-effectively using LocalStack’s cloud emulators. You’ll see how to emulate complex AI workflows, test integrations, and use “vibe coding” techniques confidently in a fully sandboxed local environment.

AWS Database Migration Service provides migration solutions from databases, data warehouses, and other types of data stores (e.g. S3, SAP). The migration can be homogeneous (source and target have the same type), but often is heterogeneous as it supports migration from various sources to various targets (self-hosted and AWS services).LocalStack supports DMS with selected use cases. In this session from LocalStack Community Meetup July '24, Mathieu Cloutier explores how to use LocalStack to migrate from a MariaDB database to an AWS Kinesis Stream. He goes over the differences between CDC and full load, and as a bonus you will see how easy it is to migrate from an external database to your Kinesis Stream — tested all on your local machine!Docs: https://docs.localstack.cloud/user-guide/aws/dms/