DB2 UDB's High-Function Business Intelligence in e-business

12 High-Function Business Intelligence in e-business

1.2.2 Advantages of BI functionality in the database engine

The advantages of executing BI functions in the database engine include:

򐂰 Superior scalability:

By executing query requests as close to the data as possible, significant

performance can be achieved thus enabling a larger workload to be serviced

with the same amount of computing resources.

Automatic exploitation of the database engine’s parallelism capabilities adds

significantly to performance and scalability of BI queries.

򐂰 Consistent results:

Evaluating BI functions in the database engine guarantees consistent results

and precision handling across all queries

򐂰 Reduced data latency:

When BI functionality is executed by analytic tools, they typically operate on

data that has been extracted and transformed from the operational system.

There is a degree of latency introduced as a consequence. Also this

intermediate extract/transform process may inhibit organizations from

performing more frequent analytics.

By supporting BI functions in the engine, organizations can potentially run

analytics:

– Directly against operational data thereby reducing the impact of latency.

– More frequently against the data which could enable an organization to

detect changing business conditions in a more timely fashion, thus gaining

a competitive advantage.

򐂰 Reliability, availability, security and maintainability:

A database engine’s inherent manageability characteristics are available to all

BI queries.

1.3 Redbook focus

In this redbook, we cover DB2 UDB engine features that have not been

adequately discussed in either the DB2 technical library or other redbooks, but

are critical to the performance and scalability of a BI system.

Our focus is on materialized views (aka Automatic Summary Tables [AST] or

Materialized Query Tables [MQT] in IBM product documentation), and the

statistics, analytic and OLAP functions in the DB2 UDB engine that can be

exploited by analytic applications, DSS, OLAP and mining tools shown in

Figure 1-5, to achieve superior performance and scalability.