Oracle Data Visualization (DV) and Oracle Business Intelligence Suite, Enterprise Edition (OBIEE) are advanced BI solutions with Oracle as the backend database for delivering such visualizations.

However, processing large amounts of data in Oracle is inherently less efficient than processing it in a faster external data processing engine. Oracle is designed for storage and retrieval, not for the large transformations that require complex tuning and continuous hardware upgrades, or expensive alternatives like Exadata or Hadoop.

Instead of burdening Oracle users or switching to appliances or other risky factories to accelerate and make architecture inefficient, centralize BI data preparation on the file system you use. Centralized data preparation frees the DB from repeated transformation activities and BI users from the burden of data integration for every reporting job. The same logic applies to Oracle Data Visualization (DV / Desktop) and other BI tools.

Core components of IRI Voracity Data management platform, such as IRI CoSort – and in particular the program SortCL – are proven offline machines for Extraction, Transformation, Load (ETL) and associated Oracle data management from Oracle (and other major Data sources. The numerous Data transformation functions SortCL runs much faster outside of Oracle, either in the file system or on Hadoop. Robust data masking and test data generation are also part of this environment. In fact, you can combine data manipulation in masking while preparing data for presentation.

In addition to versatility and speed, the operation is simple. The 4GLJob descriptions von SortCL are much simpler and smaller than PL/SQL procedures, and they can be automatically generated via a free, familiar Eclipse-GUI created, provided, and managed. Both IT (ETL) and BI users will find SortCL a simpler way to provide data for reporting and analysis.

Read this article The advantages of central data linking (or blending) for BI, using Oracle DV as an example. Consider how much earlier you can produce reusable tables and files from large datasets that are prepared externally without impacting query performance.