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/

Silvio and Carole introduce lstk, LocalStack's next-generation CLI built from scratch in Go. They explain why the team rebuilt the CLI, walk through its key features — zero-config startup, seamless browser-based authentication, and a rich interactive TUI that surfaces real-time progress and actionable errors — and demo how lstk gets you from install to a running emulator in seconds. They also dive into the architecture behind lstk and how it is designed to support multiple emulators, flexible runtimes, and deep integrations with CI pipelines and IDEs.

Cloud-first development kills your inner dev loop. There's a better way.TypeDB on AWS is powerful — Lambda functions querying complex relationship hierarchies, recursive schema functions resolving transitive memberships, all without application logic. But iterating on schemas and queries in the cloud is slow and expensive.Harsh Mishra introduces the TypeDB extension for LocalStack: run a fully functional TypeDB server inside your local AWS environment and connect your app exactly as you would in production. Faster iteration, zero unnecessary spend.

Are Property Graphs living up to the hype? Maybe the model itself is the problem.We made the move from relational databases to graph databases to escape "Join Pain" and model the real world more naturally — but for many engineering teams, that promise has curdled into something worse: the Spaghetti Graph.Complex queries. Ugly workarounds for multi-party relationships. Fragile schemas that shatter with every iteration and become a nightmare to maintain.The good news? The problem isn't your data.In this talk, Joshua Send breaks down why standard Labeled Property Graphs (LPGs) fall short when applied to complex domains — and introduces TypeDB, a strongly-typed database that brings together the connectivity of a graph with the integrity of a relational model.You'll come away understanding:Why LPGs struggle at scale and complexityWhat "Spaghetti Graphs" are and how teams fall into the trapHow TypeDB's type system enforces data integrity without sacrificing flexibilityWhen a strongly-typed graph database is the right tool for the jobWhether you're deep in a graph migration, evaluating database architectures, or just tired of schema chaos — this one's for you.