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:
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.
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
– 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
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.