Architecture for Oracle data warehouses

The architecture of an Oracle Data warehouse can be divided into three tiers:

Data acquisition Layer: Oracle database Enterprise ETL OPTION + Oracle database data Quality option in Oracle database 10g the same software is designed from data model, data Quality management, ETL process design and all the functions of metadata management. All ETL processes are available through Oracle data warehouses: Oracle Warehouse Builder generated ETL scripts are stored in Oracle 10g databases, as required by the Data Warehouse system The data is extracted and loaded into the Data warehouse system at regular intervals. Because the execution of ETL is in Oracle 10g database, can make full use of the powerful parallel processing ability that Oracle 10g database provides, guarantee the efficient and reliable execution of data acquisition.

Data storage layer: Oracle 10g database realizes the centralized storage and management of various types of data in Data Warehouse system, including various structured data and unstructured data. Oracle 10g database built-in OLAP and data mining capabilities, without the need for complex data migration, you can directly in the relational database to complete the complex statistical analysis functions. Oracle 10g databases can support the storage of large amounts of data by using partitioning technology, with a maximum data amount of 8,000 PB (1PB=1024TB). Oracle 10g provides a powerful parallel processing capability to meet the performance and scalability requirements of the Data Warehouse system. And the system manages the data warehouse through the Grid control console.

Data Presentation layer: Oracle delivers a new business intelligence solution Oracle BI EE, OLAP Analysis development tool (Jdeveloper+bi Beans), and Data mining tools (Oracle Data Miner), presenting the results of statistical analysis in a variety of ways. Oracle's data presentation solution is implemented using Java and HTML in two ways, based on the standard EE platform. The use of a unified metabase does not require the exchange of metadata to minimize the maintenance of the system. At the same time, Oracle's data presentation program provides powerful analytics and Easy-to-use analysis dashboards, and enables integration through portal technology to provide a consistent access interface for different types of users.

Related reading

A data warehouse is a theme-oriented, integrated, non-renewable, and constantly changing collection of data that is used to support decision analysis processes in enterprises or organizations.

Data Warehouse, the English name is Warehouse, can be abbreviated as DW.

The father of the Data Warehouse Bill Inmon's definition, published in 1991 in the book "Building the Data Warehouse", is widely accepted--the Data Warehouse (database Warehouse) is a theme-oriented (Subject oriented) , integrated (integrated), relatively stable (non-volatile), a collection of data that reflects historical changes (time Variant) to support management decisions (Decision making Support).