Introduction to Oracle Warehouse Builder

Oracle Warehouse Builder provides enterprise solutions for end-to-end data integration. This section introduces the range of functionality provided by Warehouse Builder.

This section contains the following topics:

• Overview of Oracle Warehouse Builder
• Data Consolidation and Integration

Overview of Oracle Warehouse Builder

Oracle Warehouse Builder is a single, comprehensive tool for all aspects of data integration. Warehouse Builder leverages Oracle Database to transform data into high-quality information. It provides data quality, data auditing, fully integrated relational and dimensional modeling, and full lifecycle management of data and metadata. Warehouse Builder enables you to create data warehouses, migrate data from legacy systems, consolidate data from disparate data sources, clean and transform data to provide quality information, and manage corporate metadata.

Data Consolidation and Integration

Many global corporations have data dispersed on different platforms using a wide variety of data reporting and analysis tools. Customer and supplier data may be stored in applications, databases, spreadsheets, flat files, and legacy systems. This diversity may be caused by organizational units working independently over a period of time, or it may be the result of business mergers. Whatever the cause of diversity, this diversity typically results in poor quality data that provides an incomplete and inconsistent view of the business.

Transforming poor quality data into high quality information requires:

• Access to a wide variety of data sources
  Warehouse Builder leverages Oracle Database to establish transparent connections to numerous third-party databases, applications, files, and data stores as listed in "Sources and Targets Supported in Warehouse Builder 11.1".
• Ability to profile, transform, and cleanse data
  Warehouse Builder provides an extensive library of data transformations for data types such as text, numeric, date, and others. Use these transformations to reconcile the data from many different sources as described in "Data Transformation".
  Before loading data into a new data store, you can optionally profile the data to evaluate its quality and appropriateness. Subsequently, you can match and merge records using rules that you devise. You can validate name and address data against postal databases. This process of changing poor quality data into high quality information is introduced in "About the Data Quality Management Process".
• Ability to implement designs for diverse applications
  Using Warehouse Builder, you can design and implement any data store required by your applications, whether relational or dimensional. The process of designing your data store is described in "Designing Target Schemas".
• Audit trails
  After consolidating data from a variety of sources into a single data store, you are likely to face the challenge of verifying the validity of the output information. For instance, can you track and verify how a particular number was derived? This is a question often posed by decision makers within your organization and by government regulators. The process of accessing deployment and auditing information is described in "Auditing Deployments and Executions".