Oracle Database 11g Release 2 for Data Warehousing and Business Intelligence

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Oracle Database 11g Release 2 for Data Warehousing and Business Intelligence.

Download White Paper

Oracle Database 11g is a comprehensive database platform for data warehousing and business intelligence that combines industry-leading scalability and performance, deeply-integrated analytics, and embedded integration and data-quality — all in a single platform running on a reliable, low-cost grid infrastructure. Oracle Database 11g provides best-of-breed functionality for data warehouses and data marts, with proven scalability to 100’s of terabytes and record-breaking performance. It also provides a uniquely integrated platform for analytics; by embedding OLAP, Data Mining, and statistical capabilities directly into the database, Oracle delivers all of the functionality of standalone analytic engines with the enterprise scalability, security, and reliability of an Oracle Database. This white-paper provides an overview of Oracle’s capabilities for data warehousing, including the Oracle Exadata Database Machine, and discusses the key features and technologies by which Oracle-based business intelligence and data warehouse systems easily integrate information, perform fast queries, scale to very large data volumes and analyze any data.

 

Courtesy: Information-Management

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CDM – Canonical Data Model

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Useful websites to understand CDM

http://www.information-management.com/issues/2007_50/10001733-1.html
http://soa-eda.blogspot.com/2007/04/how-to-mediate-semantics-in-eda.html
http://www.collibra.com/article/canonical-data-model


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Bottom Up Approach

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Bottom-Up Approach

The advantages of this approach are:

  • Faster and easier implementation of manageable piece
  • Favorable return on investment and proof of concept
  • Less risk of failure
  • Inherently incremental; can schedule important data marts first
  • Allows project team to learn and grow

The disadvantages are:

  • Each data mart has its own narrow view of data
  • Permeates redundant data in every data mart
  • Perpetuates inconsistent and irreconcilable data
  • Proliferates unmanageable interfaces

In this bottom-up approach, you build your departmental data marts one by one. You would set a priority scheme to determine which data marts you must build first. The most severe drawback of this approach is data fragmentation. Each independent data mart will be blind to the overall requirements of the entire organization.

Courtesy: Data Warehouse Fundamentals – Paulraj Ponniah

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Top Down Approach

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Top-Down Approach

The advantages of this approach are:

  • A truly corporate effort, an enterprise view of data
  • Inherently architected—not a union of disparate data marts
  • Single, central storage of data about the content
  • Centralized rules and control
  • May see quick results if implemented with iterations

The disadvantages are:

  • Takes longer to build even with an iterative method
  • High exposure/risk to failure
  • Needs high level of cross-functional skills
  • High outlay without proof of concept

This is the big-picture approach in which you build the overall, big, enterprise-wide data warehouse. Here you do not have a collection of fragmented islands of information. The data warehouse is large and integrated. This approach, however, would take longer to build and has a high risk of failure. If you do not have experienced professionals on your team, this approach could be dangerous. Also, it will be difficult to sell this approach to senior management and sponsors. They are not likely to see results soon enough.

Courtesy: Data Warehouse Fundamentals – Paulraj Ponniah

 

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