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of sophisticated tools.
4. IT or business staff are spending a lot of time developing special reports for
Building a Data Warehouse
As shown in Figure 16.14, a data warehouse is typically built by using the
1. Data selection
2. Data preprocessing/cleaning
3. Data transformation
A description of these processes has already been presented before. The
transformed data is structured in the form of multi-dimensional cubes in the data
warehouse so that different types of users (say from different department of an
organization) can view the same data in different form depending on their interest.
The data transformation tools are known as OLAP (On-Line Analytical
Processing) tools. Finally, data access tools allow the end users (decision makers)
to directly and easily access the desired information from the data warehouse
without the help of any IT staff. The overall architecture of a typical data
warehouse is shown in Figure 16.15.
Few Related Terminologies
Some commonly encountered terminologies while dealing with data warehousing
are briefly described below.
OLTP and OLAP
OLTP stands for On-Line Transaction Processing. It is the database architecture
designed specifically for transaction processing. OLTP describes systems that
support day-to-day business operations. These are transaction processing systems,
with many users concurrently accessing a database to insert, retrieve, update, and
delete individual records. Hence OLTP systems are optimized for the entry,
retrieval, and update of individual pieces of fact, not for the support of complex,
large-scale data analysis.
On the other hand, OLAP stands for On-Line Analytical Processing. It is the
database architecture designed specifically for effective data analysis. OLAP
describes systems that integrate data from one or more systems, then aggregates and transforms the data to support effective decision-making. OLAP systems are
based on the concept of multi-dimensional databases and...
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This document was uploaded on 04/07/2014.
- Spring '14