All the cool kids are talking about Data Products and Data Mesh. The data companies have gotten ahold of terms and started to say their twenty-year-old ETL tools are the perfect tools to do that fashionable product-meshy stuff. What is going on?
Besides bragging rights to your data friends, what are the principles behind these related but separate ideas? Why have data analytic teams imported concepts from software development – Projects to Products (P2P) and Domain Driven Design (DDD)? What value do they provide? What problems do they solve? Why should you care? Beyond the hype, what exactly are these ideas?
Data Mesh is a paradigm that organizes your data and analytic team’s data work into decentralized domains. Instead of boiling the data ocean, they focus on fewer data sets and fewer customers and deliver more insight.
Data Products drive the view that all our work in data: integration, modeling, visualization, and governance is part of an ongoing, ever-improving unit of value that we deliver to our customers. Instead of success being defined by completing the task on your project plan, it is defined by the actual usage and ever-expanding insight delivery to a customer.
Chris Bergh, who has been ripping off software engineering and manufacturing for ideas for years (DataOps takes liberally from DevOps and Lean), will talk about:
What exactly are Data Mesh and Data Products?
Why the principles matter, how they apply, and how to avoid pitfalls in their use.
Extended examples of successful Data Mesh, Data Products, and DataOps examples.
What do the words ‘Au Courant’ mean?