Data Warehouse Basics: Key Aspects and Data Mining

Data Warehouse Basics

A data warehouse is a central repository for storing and analyzing organizational data from multiple sources. The primary purpose of a data warehouse is not to serve transaction processing but rather to support decision making by providing access to consolidated enterprise data for analysis. This article will focus on key aspects related to Data Mining within the context of data warehouses.

Data Warehousing Overview

A data warehouse stores data extracted from various resources including transaction databases and relational databases. Its main goal is to facilitate searching and analyses, often containing large amounts of historical data. Data teams use this data for analytics and business intelligence purposes. Data warehouses are designed to feed information into decision support systems, software applications used for querying and analyzing data to assist in management decision making.

Data Management

Data warehouses receive data from a variety of sources on a regular basis, typically every few minutes. These sources may include operational databases, external partner systems, and customer-interface applications. Data is periodically pulled from these sources and made available for decision-makers to analyze. A well-designed data warehouse integrates data from varied sources into a consistent format, ensuring data is stored in a universally acceptable manner.

Data Modeling

Data warehouses are built using specific data models that facilitate efficient data access and analysis. The most common data models include the Relational OLAP (ROLAP) model and the Multidimensional OLAP (MOLAP) model. The ROLAP model is an extended relational database management system that maps multidimensional data, while the MOLAP model directly acts on multidimensional data and operations.

Data Types

There are three main types of data warehouses:

1. Enterprise Data Warehouse (EDW): A key database that facilitates decision-support services throughout the organization, providing access to cross-organizational information and offering a unified approach to data representation.
2. Operational Data Store (ODS): This type of warehouse refreshes in real-time and is often preferred for routine activities like storing employee records. It is required when data warehouse systems do not support reporting needs of the business.
3. Data Mart: A subset of a data warehouse built to maintain a particular department, region, or business unit. Every department of a business has a central repository or data mart to store data.

Data Mining

Data mining is a feature of a data warehouse that involves looking for meaningful data patterns in vast volumes of stored data. This process helps businesses analyze customers, track product performance, determine optimal pricing, evaluate promotional strategies, and analyze customer buying trends.

In summary, data warehouses play a crucial role in the digital transformation of organizations, enabling better decision-making by integrating and analyzing data from diverse sources. Additionally, they have evolved significantly over time, with cloud-based solutions becoming increasingly popular due to their scalability and cost-effectiveness.