Summary

Large organizations evolve and their analytics journeys differ. A single data lakehouse will not be able to cater to all the analytical requirements of the organization. Therefore, the data lakehouse architecture needs to be scaled in a governed manner to address the ever-changing analytical requirements. In addition, the data in the data lakehouse needs to be democratized and enable structured data sharing between the different units of the organization. This chapter covered the methods for scaling the data lakehouse architecture pattern.

The chapter started by emphasizing the need for macro patterns. We defined the two categories of units that embody a large organization and the five key considerations that influence the analytical requirements of these units. The next section of the chapter focused on implementing the two general macro-architecture patterns. The hub-spoke pattern was the first pattern that we discussed. The section covered the key components that develop...