Data Mesh

Chapter 7. After the Inflection Point

The only way to make sense out of change is to plunge into it, move with it, and join the dance.

Alan Watts

Standing at an inflection point is a magical experience. It’s where we look at what has come before, learn from it, and choose a new path. It’s a point where we have a choice to turn to a new direction, with an eye on a different destination. This chapter introduces the destination and the outcomes to expect when choosing data mesh at your organization’s inflection point.

Data mesh assumes the environmental conditions I introduced in the previous chapter as a default state. By default, data mesh assumes the ubiquitous nature of data. Data can be of any origin; it can come from any system within an organization, or outside, and across boundaries of organizational trust. Any underlying platform can serve it on one cloud hosting service or another. Data mesh assumes the diversity of data use cases and their unique modes of access to data. The data use cases range from historical data analysis and reporting to training machine learning models and data-intensive applications. And lastly, data mesh assumes complexity of the business landscape—continuous growth, change, and diversity—as a natural state of being.

Data mesh learns from the past solutions and addresses their shortcomings. It reduces points of centralization that act as coordination bottlenecks. It finds a new way of decomposing the data architecture without slowing ...