Business Intelligence Tutorial
In this section, you will obtain an overview of data
warehousing and the data warehousing tasks in this tutorial.
The systems that contain operational data--the data
that runs the daily transactions of your business--contain information
that is useful to business analysts. For example, analysts can use
information about which products were sold in which regions at which time of
year to look for anomalies or to project future sales. However, there
are several problems if analysts access the operational data directly:
- They might not have the expertise to query the operational
database. For example, querying IMS databases requires an application
program that uses a specialized type of data manipulation language. In
general, those programmers who have the expertise to query the operational
database have a full-time job in maintaining the database and its
applications.
- Performance is critical for many operational databases, such as databases
for a bank. The system cannot handle users making ad-hoc
queries.
- The operational data generally is not in the best format for use by
business analysts. For example, sales data that is summarized by
product, region, and season is much more useful to analysts than the raw
data.
Data warehousing solves these problems. In data
warehousing, you create stores of informational
data--data that is extracted from the operational data and then
transformed for end- user decision making. For example, a data
warehousing tool might copy all the sales data from the operational database,
perform calculations to summarize the data, and write the summarized data to a
separate database from the operational data. End users can query the
separate database (the warehouse) without impacting the operational
databases.
DB2 Universal Database offers the Data Warehouse Center, a DB2
component that automates warehouse processing. You can use the Data
Warehouse Center to define which data to include in the warehouse.
Then, you can use the Data Warehouse Center to automatically schedule
refreshes of the data in the warehouse.
This tutorial covers the most common tasks that are required to set up a
warehouse.
In this tutorial, you will:
- Define a subject area that identifies and groups the processes
that you will create for the tutorial.
- Explore the source data (which is the operational data) and define
warehouse sources. Warehouse sources identify the source
data that you want to use in your warehouse
- Create a database to use as the warehouse and define warehouse
targets, which identify the target data to include in your
warehouse.
- Specify how to move and transform the source data into its format for the
warehouse database. You will define a process, which
contains the series of movement and transformation steps required to produce a
target table in the warehouse from one or more source tables, views, or
files. You will then divide the process into steps, each of
which defines one operation in the movement and transformation process.
Then you will test the steps that you defined and schedule them to run
automatically.
- Administer the warehouse by defining security and monitoring database
usage.
- Create an information catalog of the data in the warehouse if you have
installed the DB2 Warehouse Manager package. An information
catalog is a database that contains business metadata that helps users
identify and locate data and information that is available to them in the
organization. End users of the warehouse can search the catalog to
determine which tables to query.
- Define a star schema model for the data in the warehouse. A
star schema is a specialized design that consists of multiple
dimension tables, which describe aspects of a business, and one
fact table, which contains the facts about the business. For
example, if you manufacture soft drinks, some dimension tables are products,
markets, and time. The fact table might contain transaction information
about the products that are ordered in each region by season.
- You can join the fact table and dimension tables to combine details from
the dimension tables with the order information. For example, you might
join the product dimension with the fact table to add information about how
each product was packaged to the orders.
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