Summary

One needs to grasp the concept of the data mesh and its core principles but apply practical data fabric DataOps features to make the data mesh implementation work as expected.

Data being a product makes sense, but it needs to be fabricated, produced, and curated to the point that data-derived information is ready for self-service, which can be leveraged for knowledge pop-out insights, leading to wise decisions. This progression in the past was forced through big systems software development, but today and going forward, it will involve automated intelligence brought about by the data engineer.

Data needs to stand alone and be smart as well as enabled for insight harvesting. Data needs to be smarter than it is today! The wording of that implies a personification of data. This is a data engineer’s goal. Engineered data is no longer hindered by today’s big data approaches. Data that is of the highest quality, fully understood, linkable, transparently curated...