Databases Reference
In-Depth Information
FIGURE 10.5
Semantic layer integration for data visualization.
The goal of using the workload quadrant is to identify the complexities associated with the
data processing and how to mitigate the associated risk in infrastructure design to create the next-
generation data warehouse.
Analytics
Identifying and classifying analytical processing requirements for the entire set of data elements
at play is a critical requirement in the design of the next-generation data warehouse platform. The
underpinning for this requirement stems from the fact that you can create analytics at the data dis-
covery level, which is very focused and driven by the business consumer and not aligned with the
enterprise version of the truth, and you can equally create analytics after data acquisition in the data
warehouse.
Figure 10.5
shows the analytics processing in the next-generation data warehouse platform. The
key architecture integration layer here is the data integration layer, which is a combination of seman-
tic, reporting, and analytical technologies, which is based on the semantic knowledge framework,
which is the foundation of next-generation analytics and business intelligence. This framework is dis-
cussed later in this chapter.
Finalizing the data architecture is the most time-consuming task, which once completed will
provide a strong foundation for the physical implementation. The physical implementation will be
accomplished using technologies from the earlier discussions including Big Data and RDBMS
systems.
Physical component integration and architecture
The next-generation data warehouse will be deployed on a heterogeneous infrastructure and archi-
tectures that integrate both traditional structured data and Big Data into one scalable and performing
