Data Warehouse Architectures

Data warehouses and their architectures vary depending upon the specifics of an
organization's situation. Three common architectures are:

1. Data Warehouse Architecture (Basic)
2. Data Warehouse Architecture (with a Staging Area)
3. Data Warehouse Architecture (with a Staging Area and Data Marts)

Data Warehouse Architecture (Basic)
In Figure, the metadata and raw data of a traditional OLTP system is present, as
is an additional type of data, summary data. Summaries are very valuable in data
warehouses because they pre-compute long operations in advance. For example, a
typical data warehouse query is to retrieve something like August sales. A
summary in Oracle is called a materialized view.

Data Warehouse Architecture (with a Staging Area)


You need to clean and process your operational data before putting it
into the warehouse. You can do this programmatically, although most data
warehouses use a staging area instead. A staging area simplifies building
summaries and general warehouse management.

Data Warehouse Architecture (with a Staging Area and Data Marts)

You may want to customize your warehouse’s architecture for different groups within your
organization. You can do this by adding data marts, which are systems designed for
a particular line of business. Above figure illustrates an example where purchasing,
sales, and inventories are separated. In this example, a financial analyst might want
to analyze historical data for purchases and sales.

Difference between OLTP and Data Warehouse

One major difference between the types of system is that data warehouses are not
usually in third normal form (3NF), a type of data normalization common in OLTP
environments.
Following figure illustrates key differences between an OLTP system and a data
warehouse.

Basic Concepts:

This section introduces basic data warehousing concepts:

What is a Data Warehouse?
A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.

A common way of introducing data warehousing is to refer to the characteristics of a data warehouse as set forth by William Inmon:

1. Subject Oriented
2. Integrated
3. Nonvolatile
4. Time Variant

Subject Oriented

Data warehouses are designed to help you analyze data. For example, to learn more about your company’s sales data, you can build a warehouse that concentrates on sales. Using this warehouse, you can answer questions like "Who was our best customer for this item last year?" This ability to define a data warehouse by subject matter, sales in this case, makes the data warehouse subject oriented.

Integrated
Integration is closely related to subject orientation. Data warehouses must put data from disparate sources into a consistent format. They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated

Nonvolatile
Nonvolatile means that, once entered into the warehouse, data should not change. This is logical because the purpose of a warehouse is to enable you to analyze what has occurred.

Time Variant
In order to discover trends in business, analysts need large amounts of data. This is very much in contrast to online transaction processing (OLTP) systems, where performance requirements demand that historical data be moved to an archive. A data warehouse’s focus on change over time is what is meant by the term time variant.