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What is Data
Warehousing?

The data warehouse (DWH) is a repository of an organization’s electronically stored data, extracted from operational systems and made available for ad-hoc queries and scheduled reporting

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Often people are confused between data warehouses and databases as they share some similarities. So, what distinguishes the two? The main difference between a DWH and a database becomes evident when an enterprise performs analytics on an extensive data set. In such a case, a DWH is equipped to handle a large data set, but a database is not.

Data Warehouse Architecture

A data warehouse architecture uses dimensional models to identify the best technique for
extracting meaningful information from raw data and translating it into an easy-tounderstand structure. However, you should keep in mind three main types of architecture when designing a business-level real-time data warehouse.

  • Single-tier Architecture
  • Two-tier Architecture
  • Three-tier Architecture

Enlisting the Features

The key features of a data warehouse include

Subject Oriented

It provides information catered to a specific subject-area instead of the whole organization’s ongoing operations.
Examples of subjects include product information, sales data, customer, supplier details, etc.

Integrated

It is developed by combining data from multiple sources, such as flat files and relational databases, which
offers better data analysis.

Time-Variant

The data in a DWH gives information from a specific historical point of time; therefore, the data is categorized with a particular time
frame.

Non-volatile

Non-volatile refers to historical data that is not omitted when newer data an operational database, which means that any regular changes in the operational database are seen in the data warehouse.

Why do Businesses Need Data
Warehousing and Business Intelligence?

A lot of business users wonder why data warehousing is essential. The simplest way to explain this is through the various benefits to the end-users.

Improved end-user access to a wide variety of enterprise data

Increased data consistency

Additional documentation of the data

Potentially lower computing costs and increased productivity

Providing a place to combine related data from separate sources

Creation of a computing infrastructure that can support changes in computer systems and business structures

Empowering end-users to perform ad-hoc queries or reports without impacting the performance of the operational systems

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