In this talk Rebecca presents some principles of evolutionary architecture that allow systems to respond to change without needing to predict the future. We then briefly describe approaches that realize these principles and discuss how these approaches support adaptability of systems in an evolutionary way.
Architecture has lots of difficult problems, which this talk highlights by investigating what makes architecture so hard. At the core of many architectural problems: getting good granularity, which we illustrate via event-driven architectures, teams, components, architectural quantum, and a host of other examples.
Defining infrastructure as code should make systems consistent, reliable, and easy to manage. In order to routinely change, extend, and improve infrastructure, the team needs to have confidence that changes will work correctly, and that the impact of failures is low and easily corrected. This creates a virtuous cycle of continuously improving the quality of the systems.
Data Mesh is an alternative sociotechnical approach in managing analytical data. Its objective is enabling access to high quality data for analytical and machine learning use cases - at scale. This is a well-rounded introductory talk to Data Mesh. Why you might need one, what it is and how to get started with implementing it.