Data Management at Scale, 2nd Edition

Chapter 2. Organizing Data Using Data Domains

The trends discussed in Chapter 1 require us to rethink the way data management is done. We’ve talked about the tight couplings that arise when making exact copies of application data and the difficulties of operationalizing analytics on raw data. We’ve also looked at the unification problems, the tremendous effort of building an integrated data warehouse, and its impact on agility. As discussed in Chapter 1, we need to shift toward an approach that enables domains, teams, and users to distribute, consume, and use data themselves easily and securely. We need a strategy and organizational change that moves data closer to the business. We need platforms, processes, patterns, and standard interfaces that simplify the work for others. We need a data management architecture that works at scale. This chapter discusses this further, starting with how to organize the landscape using data domains.

Before we delve into that topic, however, we’ll explore a number of generally acknowledged principles of how applications are designed and work together. After that, we’ll zoom in on the inner architecture of applications and discover what we can learn from that. Then we’ll turn our attention to domains, looking at domain-driven design and business architecture. We’ll discuss different characteristics of domains, key data management principles, and domain ownership responsibilities. By the end of this chapter, you’ll understand what data domains are ...