2. Data Warehousing and Dimensional Modelling :

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.

In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users.

As per Bill Inmon who is regarded as father of Data Warehousing, “A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process”.

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 analyse 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.

Ralph Kimball and Bill Inmon are considered most respected thought leaders in Data warehouse and Business Intelligence space.

There is no right or wrong approach. But more and more datawarehouse systems have evolved into dimensional model as propounded by Ralph Kimball.

What is dimensional modelling?

Dimensional modelling is widely accepted as the preferred technique for presenting analytic data. It addresses two simultaneous requirements:

  1. Deliver data that’s understandable to the business users.
  2. Deliver fast query performance.

In case after case, for more than five decades, IT organizations, consultants, andbusiness users have naturally gravitated to a simple dimensional structure to matchthe fundamental human need for simplicity. Simplicity is critical because it ensuresthat users can easily understand the data, as well as allows software to navigate anddeliver results quickly and efficiently. The ability to visualize something as abstract as a set of data in a concrete and tangible way is the secret of understandability.

Another requirement is faster query performance. Normalized structure (or ER Model) are immensely useful for operational processing because insert and update transaction touches the database in only one place. Normalized models, are too complicated for BI queries. Users can’t understand normalized models that resemble a complex business application such as typical organization Sales metrics revenue and profits by year/quarter/product,/region/ category/sales executive. Most relational database management systems can’t efficiently query a normalized model; the complexity of users’ unpredictable queries overwhelms the database optimizers, resulting in disastrous query performance.  Dimensional model addresses overly complex schemas in the presentation area.

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