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!

With the growing Serverless workloads, managing and maintaining them is best recommended with Infrastructure as Code (IaC). While this holds the complete infrastructure and its configurations, we could have events from one service destined to another via configuration. When building these configurations, we could also reduce the application code making it more maintainable and scalable. In this session, Jones walked us through a fully end-to-end solution built with Amazon EventBridge and AWS Step Functions with SDK integrations which have helped him to improvise the application with just IaC and very minimal application code.

Tired of rebuilding your stack from scratch every time you run tests or restart your dev environment? In this episode, we dive into Cloud Pods, LocalStack’s powerful state management feature. You’ll learn how to snapshot your entire LocalStack environment (services, resources, data), restore that exact state across machines, teams, or CI runs, speed up test cycles and workflows, and use Cloud Pods to make testing in CI/CD faster and more reliable. Cloud Pods let you freeze your infrastructure in time. Perfect for repeatable tests, isolated dev environments, or onboarding new teammates.

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, and iterate quickly on transformations before pushing them to production. Through 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.