Databases Reference
In-Depth Information
FIGURE 11.2
Enterprise use of data before Big Data.
investigation and analysis. The downside of the process shown in
Figure 11.2
is the isolation of each
layer of the system, resulting in duplication of data and incorrect attribution of the data across the dif-
ferent systems.
The situation shown in
Figure 11.2
continues to happen with the best data governance programs
implemented, due to the fact that organizations continue to ignore the importance of corporate meta-
data and pay the penalty once incorrect information is processed into the systems from source sys-
tems all the way to business intelligence platforms.
Figure 11.3
shows the data-driven architecture that can be deployed based on the metadata and
master data solutions. This approach streamlines the data assets across the enterprise data warehouse
and enables seamless integration with metadata and master data for data management in the data
warehouse. While this architecture is more difficult to implement, it is a reusable approach where new
data can be easily added into the infrastructure since the system is driven by data-driven architecture.
Extending this concept to new systems including Big Data is more feasible as an approach. Let us
take a quick look at processing traditional data with metadata and master data before we dive into
applying this approach to processing Big Data and enabling the next-generation data warehouse to be
more data driven and agile.
Figure 11.4
shows the detailed processing of data across the different stages from source sys-
tems to the data warehouse and downstream systems. When implemented with metadata and master
