Data Warehouse the name was given by Bill Inmon in the year 1990. Ralph Kimball and Bill Inmon are the pioneers in coining most of the data warehousing concepts.
A data warehouse is a collection of subject oriented, integrated, non -volatile and time variant data to support any kind of business decision making.

• Subject Oriented: The data that provides information about a particular subject.
• Integrated: Data gathered from multiple data sources and merged into a single data store.
• Non-Volatile: Data in the data warehouse is stable, new data may be added but no data would be removed.
• Time Variant: Each data identified by a time period.

What is the need of a data warehouse when there is already an OLTP system?

Each company would own its own OLTP system with their own applications and databases. Due to merging and acquisition of smaller corporations, the company would end up having different information stored in different databases and monitored by different applications. For analysis and business making purpose it would be difficult to gather information from all the different data sources and understand the data. Here comes the clear need for a data warehouse implementation.

Components of a Data Warehouse:
Data warehouse is nothing but all the company information are organized and stored in a single database which is treated as the company’s data warehouse.

The process of bringing in the data from various sources into a single source is called ETL, Extraction from different sources; Transformation of the field into a centralized business logic and Loading into the database.

The process of providing analytics and decision making information from the data warehouse with the help of various reports and querying tools is called BI, Business Intelligence.

ETL and BI are the major components along with metadata management, data dictionaries, and Operational data sources form a complete enterprise data warehouse. EBI (Enterprise Business Intelligence) or EDW (Enterprise Data Warehouse) is the combination of multiple data marts. Each data mart by them self is a small data warehouse which is specific to a single department or single subject of the corporation Ex: Sales data mart, Customer data mart.

Designing a Data warehouse:

Dimension modeling is the modeling technique followed to design an EDW. Dimension modeling considers each dimension of the business and de normalizes the data for storage. Star Schema and Snow flake schema are the popular modeling schemas followed in designing a data warehouse.

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