Data mesh is one of the best ways to make analytical data clear, concise, and easy to use. Though initially limited to the analytical domain, we can easily apply it ""off-label"" to significantly improve data access in everyday operational use cases - serving customer requests and reacting to business changes in real time. The key? Using event streams and event-driven architectures as the foundation of your data mesh.

We promote data to a first-class citizen, creating data products on par with any other product in your organization. This requires renegotiating responsibilities, creating data contracts, providing technical support, and applying a consistent change management process. In return for your efforts, you get a decoupled data communication layer, providing discoverability, accessibility, and usability of business data from all across your organization.

An event-driven data mesh provides unparalleled operational and strategic flexibility to respond to ever-changing business conditions. Create new operational services and analytical jobs to pull in historical and real-time data from event streams, joining, merging, and remodeling the data as you see fit for your own business needs.

Adam provides you with a set of practical guidelines for implementing your own minimally viable data mesh. He also covers the main social and technical hurdles that you'll encounter as you implement your own data mesh, along with best practices for building and modeling your data.

What do the 640K memory limitation and the overlay technology from the 1980s have to do with today's software development? At first glance, nothing at all. Back then, both topics were answers to technical constraints that made developing hardware and software challenging. At a second glance, however, there is very much a connection. We still have many challenges. The numbers and drivers may have changed, but we still have boundaries to confront and overcome. This keynote reflects in an anecdotal and personal way on the journey that software development has been on over the past decades and the lessons learned along the way: from programming paradigms and languages to software architecture and agility to technologies like the cloud and the edge to topics like business agility and sustainability. There and back again.