Oracle Data Warehouse Tuning for 10g
暫譯: Oracle 10g 數據倉儲調優

Description:

“This book should satisfy those who want a different perspective than the official Oracle documentation. It will cover all important aspects of a data warehouse while giving the necessary examples to make the reading a lively experience.”
- Tim Donar, Author and Systems Architect
for Enterprise Data Warehouses

Tuning a data warehouse database focuses on large
transactions, mostly requiring what is known as throughput. Throughput is the passing of large amounts of information through a server, network
and Internet environment, backwards and forwards, constantly! The ultimate objective of a data warehouse is the production of meaningful and useful reporting, from historical and archived data. The trick is to make the reports print within an acceptable time frame.

A data model contains tables and relationships between tables. Tuning a data model involves Normalization and Denormalization. Different approaches are required depending on the application, such as OLTP or a Data Warehouse. Inappropriate database design can make SQL code impossible to tune. Poor data modeling can have a most profound effect on database performance since all SQL code is constructed from the data model.

 

Table of Contents:

Contents at a Glance

Chapter 1. Introduction to Data Warehousing
Chapter 2. Data Warehouse Data Modeling
Chapter 3. Making Hardware and I/O Perform in a Data Warehouse
Chapter 4. Data Warehouse Physical Architecture
Chapter 5. Effective Data Warehouse Indexing
Chapter 6. Constraints and Integrity in Data Warehouses
Chapter 7. Using Partitioning and Tuning Parallel Processing
Chapter 8. Materialized Views and Query Rewrites
Chapter 9. Oracle Dimension Objects
Chapter 10. Data Loading: Extraction and Transportation
Chapter 11. Data Loading: Loading and Transformation
Chapter 12. SQL Aggregation Using GROUP BY Clause Extensions
Chapter 13. Analysis Reporting Using Special SQL Functions
Chapter 14. SQL and the MODEL Clause
Chapter 15. OLAP (Online Analytical Processing) and Mining the Redo Logs with Data Miner
Appendices