The data warehouse environment includes a collection of architectural components that need to be organized to meet the needs of the enterprise. Figure 82 depicts the architectural components of the DW/BI and Big Data Environment discussed in this section. The evolution of Big Data has changed the DW/BI landscape by adding another path through which data may be brought into an enterprise.

This Figure also depicts aspects of the data lifecycle. Data moves from source systems into a staging area where it may be cleansed and enriched as it is integrated and stored in the DW and/or an ODS. From the DW, it may be accessed via marts or cubes and used for various kinds of reporting. Big Data goes through a similar process but with a significant difference: while most warehouses integrate data before landing it in tables, Big Data solutions ingest data before integrating it. Big Data BI may include predictive analytics and data mining, as well as more traditional forms of reporting. (See Chapter 14.)

Source Systems, on the left side of this Figure, include the operational systems and external data to be brought into the DW/BI environment. These typically include operational systems such as CRM, Accounting, and Human Resources applications, as well as operational systems that differ based on industry. Data from vendors and external sources may also be included, as may DaaS, web content, and any Big Data computation results.

Data integration covers Extract, Transform, and Load (ETL), data virtualization, and other techniques of getting data into a common form and location. In a SOA environment, the data services layers are part of this component. In this Figure, all the arrows represent data integration processes. (See Chapter 8.)