Page 165 - Building Big Data Applications

164 Building Big Data Applications

FIGURE 9.2 Enterprise use of data pre big data.

The situation shown in Fig. 9.1 continues to happen with the best data governance
programs implemented due to the fact that organizations continue to ignore the
importance of corporate metadata and pay the penalty once incorrect information is
processed into the systems from source systems all the way to business intelligence
platforms.
Fig. 9.3 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 meta-
data and master data for data management in the data warehouse. While this ar-
chitecture is more difficult to implement, it is a reusable approach where new data
canbeeasilyaddedintothe infrastructure as 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.

Master Data
Data Analytics
Data Data Discovery Data
Acquisition and Analysis Transformation
MetaData Reporting
FIGURE 9.3 Enterprise data-driven architecture.