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.

Real cloud developers aren’t pushing straight to AWS.They’re building and testing everything locally before a single deploy goes live.This episode breaks down the modern cloud dev workflow and how tools like LocalStack make it possible to move fast without burning money (or trust).Learn how local-first dev culture is changing the cloud game.

LocalStack Applications in Developer Hub provides sample templates to help LocalStack users adopt real-world scenarios to rapidly and conveniently create, configure, and deploy applications locally. ## Getting startedIn this demo, we will setup a Sagemaker on Localstack

What if your AI coding assistant could not only write infrastructure code, but also deploy it, test it, and fix issues automatically — all on your local machine? That's exactly what the LocalStack MCP Server makes possible.In this session, we'll introduce the LocalStack Model Context Protocol (MCP) Server, a new tool that lets AI agents manage your entire local cloud development lifecycle through a conversational interface. You'll learn:What MCP is and why it's a game-changer for AI-assisted developmentHow the LocalStack MCP Server turns manual cloud tasks into automated workflowsHow to set up and configure the server with your favorite AI editor (Cursor, VS Code, etc.)Real-world demos: deploying CDK apps, analyzing logs, running chaos tests, managing state with Cloud Pods, and more.Through hands-on examples, we'll walk through a complete workflow where an AI agent deploys a serverless application, verifies resources, troubleshoots issues, and tests resilience, all without leaving the conversation.If you've ever wished your AI assistant could do more than just generate code, this talk will show you what's possible when agents can actually manage your local cloud environment.