Summary

We started with understanding the challenges of data sharing in a data mesh and what in-place sharing is, defined by the data mesh architecture as the best way to share data to reduce data movement. There are many ways of sharing data across the data mesh and beyond the data mesh. We saw four of the most popular topologies for this: in-place, data pipelines, data APIs, and data sharing. We looked at the pros and cons of each along with their ideal application. One important takeaway from this chapter is that there is no one preferred way to share data. You need to understand the pros and cons of each method and then form a best practice across the data mesh for data product teams to pick the right method for their requirements.

This ends the important topics of designing and implementing a data mesh. The next four chapters will cover some common data analytics workloads and the required architecture to implement these analytical solutions on Microsoft Azure. The first scenario...