What is Data
Warehousing?

A 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. The data in a data warehouse comes in handy for business analysis and decision-making purposes.

Data warehousing involves extracting, transforming, and loading data from various sources into the data warehouse. This practice typically revolves around hardware, software, and best practices to ensure that the data is accurate, consistent, and accessible to those who need it.

Difference between Data Warehouses and Databases

Often people are confused between data warehouses (DWH) and databases since both share some similarities. So, what distinguishes the two? A database stores current and active data, whereas a data warehouse stores historical data. Additionally, data warehouses are designed for in-depth data analysis, such as reporting, forecasting, and analytics.

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-to-understand structure. However, you should understand 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 entire organization’s ongoing operations. Examples of subjects include product information, sales data, customer and supplier details, etc.

Integrated

The integrated version combines data from multiple sources, such as flat files and relational databases, which offers better data visibility and analysis.

Time-Variant

The data in a DWH provides information from a specific historical point in time. Hence, the data is categorized, keeping in view that within a particular timeframe.

Non-volatile

Non-volatile means that historical data is retained and not affected by the addition of new data to the operational database, ensuring that any updates or modifications made are reflected in the data warehouse without impacting the integrity of the existing historical data.

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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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What is Data
Warehousing?

Difference between Data Warehouses and Databases

Data Warehouse Architecture