Tag: bigdata

The rise of Predictive Analytics in business will generate many opportunities for data managers. The Business Ecosystem has arrived, and Predictive Analytics models are the “windows” into understanding its operational complexity. The Operational Data Store (ODS) is an elaboration of data warehousing design to serve the needs of business data users better (cf. Kent Graziano’s talk). The Analytical Data Mart (ADM) serves an analogous purpose to serve the needs of predictive analytics. Differences between the organization of data in an EDW or ODS and that needed to serve analytics efficiently are discussed. The discussion will include some specific data transforms required by analytical algorithms, and how the “heavy lifting” of their processing can be committed to data mart operations,. An example of the blending of an ODS and an ADM to serve predictive analytics modeling operations in a Santa Barbara bank will be presented.

At a past client, in order to meet timelines to fulfill urgent, unmet reporting needs, we found it necessary to build a virtualized Operational Data Store (ODS) as the first phase of a new Data Vault 2.0 project. This allowed us to deliver new objects, quickly and incrementally to the report developer so we could quickly show the business users their data. In order to limit the need for refactoring in later stages of the data warehouse development, we chose to build this virtualization layer on top of a Type 2 persistent staging layer. All of this was done using Oracle SQL Developer Data Modeler (SDDM) against (gasp!) a MS SQL Server Database. In this talk I will show you the architecture for this approach, the rationale, and then some of the tricks I used in SDDM to build all the stage tables and views very quickly. In the end you will see actual SQL code for the virtual ODS that you can leverage for your own projects.