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Unit 1: Data Warehouse Practice

3. Time-variant: Data are stored to provide information from a historical perspective (e.g., notes
the past 5-10 years). Every key structure in the data warehouse contains, either implicitly
or explicitly, an element of time.
4. Non-volatile: A data warehouse is always a physically separate store of data transformed
from the application data found in the operational environment. Due to this separation, a
data warehouse does not require transaction processing, recovery, and concurrency control
mechanisms. It usually requires only two operations in data accessing: initial loading of
data and access of data.

Example: A typical data warehouse is organised around major subjects, such as customer,
vendor, product, and sales rather than concentrating on the day-to-day operations and transaction
processing of an organization.

1.1.1 use of Data Warehouses in organisations

Many organisations are creating data warehouse to support business decision-making activities
for the following reasons:
1. To increasing customer focus, which includes the analysis of customer buying patterns
(such as buying preference, buying time, budget cycles, and appetites for spending),
2. To reposition products and managing product portfolios by comparing the performance
of sales by quarter, by year, and by geographic regions, in order to fine-tune production
strategies,
3. To analyzing operations and looking for sources of profit,
4. To managing the customer relationships, making environmental corrections, and managing
the cost of corporate assets, and

5. Data warehousing is also very useful from the point of view of heterogeneous database
integration. Many organisations typically collect diverse kinds of data and maintain
large databases from multiple, heterogeneous, autonomous, and distributed information
sources.

1.1.2 Query Driven approach versus update Driven approach for
heterogeneous Database integration

For heterogeneous database integration, the traditional database implements query-driven
approach, which requires complex information filtering and integration processes, and competes
for resources with processing at local sources. It is inefficient and potentially expensive for
frequent queries, especially for queries requiring aggregations.

In query-driven approach, data warehousing employs an update-driven approach in which
information from multiple, heterogeneous sources is integrated in advance and stored in a
warehouse for direct querying and analysis. In this approach, a data warehouse brings high
performance to the integrated heterogeneous database system since data are copied, preprocessed,
integrated, annotated, summarised, and restructured into one semantic data store. Furthermore,
query processing in data warehouses does not interfere with the processing at local sources.
Moreover, data warehouses can store and integrate historical information and support complex
multidimensional queries. As a result, data warehousing has become very popular in industry.

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