Both the data warehouse and data lake approaches aim to unify all of the enterprise’s data into one big model. The resultant analytical model is ineffective for all the same reasons as an enterprise-wide operational model is. Furthermore, gathering data from all systems into one location blurs the ownership boundaries of the various data elements.

Instead of building a monolithic analytical model, the data mesh architecture calls for leveraging the same solution we discussed in Chapter 3 for operational data: use mul‐ tiple analytical models and align them with the origin of the data. This naturally aligns the ownership boundaries of the analytical models with the bounded contexts’ boundaries, as shown in Figure 16-12. When the analysis model is decomposed

according to the system’s bounded contexts, the generation of the analysis data becomes the responsibility of the corresponding product teams.

Figure 16-12. Aligning the ownership boundaries of the analytical models with the bounded contexts’ boundaries

Each bounded context now owns its operational (OLTP) and analytical (OLAP) mod‐ els. Consequently, the same team owns the operational model, now in charge of transforming it into the analytical model.