ODI - Typical Applications / Goals

About

The goal of Oracle Data Integrator is to automate information exchange between applications :

  • whether for full Enterprise Application Integration (EAI)
  • or simply to populate different tables in a Data Warehouse.

Some integration needs are data oriented, especially those involving large data volumes. Other integration projects lend themselves to an event-oriented architecture for asynchronous or synchronous integration. (Changes tracked by Changed Data Capture constitute data events).

Example of Applications

Oracle Data Integrator is well suited to a broad range of integration projects– ETL, Data Migration, Master data management, Business Activity Monitoring (BAM), Business Process Management (BPM), Business Process Reengineering (BPR), and Web Services integration – implemented using a combination of Data-oriented, Event-oriented, and Service-oriented mechanisms.

Data Warehousing and Business Intelligence

by executing high-volume, high-performance loading of data warehouses, data marts, On Line Analytical Processing (OLAP) cubes, and analytical applications. It transparently handles incremental loads and slowly changes dimensions, manages data integrity and consistency, and analyzes data lineage.

Master Data Management

Master Data Management—by providing a comprehensive data synchronization infrastructure for customers who build their own data hubs, work with packaged MDM solutions, or coordinate hybrid MDM systems with integrated SOA process analytics and Business Process Execution Language (BPEL) compositions.

Service-oriented architecture

by calling on external services for data integration and by deploying data services and transformation services that can be seamlessly integrated within an SOA infrastructure. It adds support for high-volume, high-performance bulk data processing to an existing Service-oriented architecture.

Data Migration

  • with or without subsequent replication between the old and the new system
  • by providing efficient bulk load of historical data (including complex transformations) from existing systems to new ones. It continues to seamlessly synchronize data for as long as the two systems coexist.

Others

  • Point-to-point Data Integration
  • Data Replication
  • Data federation,
  • Data synchronization

Powered by ComboStrap