This tutorial starts with the introduction to Data Warehousing, Defination of OLAP, difference between Data warehouse and the OLTP Database, Objectives of data warehousing and data flow.
Computerization of business processes; technological advances in transmission and storage of data; and powerful database management tools have opened up new possibilities of data manipulation and analysis. Business managers are eager to explore the repositories of current and historical data to identify trends and patterns in the wrap and hoof of business. They hope to mine data and use them for taking intelligent business decisions. In this context, industries are increasingly focusing on data warehousing, Online Analytical Processing (OLAP), and other related technologies.
What's the Difference
‘Data warehouse’ and ‘OLAP’ are terms which are often used interchangeably. Actually they refer to two different components of a decision support system. While data in a data warehouse is composed of the historical data of the organization stored for end user analysis, OLAP is a technology that enables a data warehouse to be used effectively for online analysis using complex analytical queries. The differences between OLAP and data warehouse is tabulated below for ease of understanding:
Data Warehouse
Data from different data sources is stored in a relational database for end use analysis
Data from different data sources is stored in a relational database for end use analysis Data is organized in summarized, aggregated, subject oriented, non volatile patterns.
Data is a data warehouse is consolidated, flexible collection of data Supports analysis of data but does not support online analysis of data.
Online Analytical Processing
A tool to evaluate and analyze the data in the data warehouse using analytical queries.
A tool which helps organize data in the data warehouse using multidimensional models of data aggregation and summarization.
Supports the data analyst in real time and enables online analysis of data with speed and flexibility.
Ralph Kimball the co-founder of the data warehousing concept has defined the data warehouse as a “"a copy of transaction data specifically structured for query and analysis”.
Both definitions highlight specific features of the data warehouse. The former definition focuses on the structure and organization of the data and the latter focuses upon the usage of the data. However, a listing of the features of a data warehouse would necessarily include the aspects highlighted in both these definitions.
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Defining OLAP Solutions and Data Warehouse design
This tutorial covers OLAP solutions used by Data warehouses and understanding Data Warehouse design. The enterprise needs to ask itself certain fundamental questions before actually launching on the process of designing the data warehouse. It must begin with a conviction that a data warehouse would really help its business and the return on investment will make it worth it.
Data Warehouse Database and OLTP Database
In this tutorial we will learn about the differences between Data Warehouse database and OLTP database and the objectives of a Data warehouse and Data flow. The data warehouse and the OLTP data base are both relational databases. However, the objectives of both these databases are different.
