Being a technology consultant I get to meet a lot of people everyday. I have found that some are very knowledgeable about technology and software and how a particular product will be able to help them; whereas some have been totally clueless about new products, technologies and concepts that can make their business more efficient. I have decided to write a series of articles called the ‘Clueless Guide’ that will basically break down the technologies and concepts in a simpler form.
As part of the ‘basic’ series, we now come to the world of OLAP and the various concepts, technologies and tools in it. In my previous post on OLTP and OLAP, we saw the difference (conceptually) between what comprises an OLTP system and what would be considered as an OLAP system.
Having understood the fundamental concept of OLAP, lets go down the rabbit hole and see what other wonders we find. To jumpstart the process I’ll throw at you the 3 acronyms you are bound to hear when OLAP pops up and that is MOLAP, ROLAP and HOLAP. They are the three stooges of OLAP.
Ralph Kimball’s definition from his first edition of The Data Warehouse Toolkit is:
A data warehouse is a copy of transaction data specifically structured for query and analysis.
The beauty of this definition is its simplicity. There probably is no definition for a typical data warehouse.
People are still confused when it comes to attributing what constitutes an OLTP or an OLAP system. The basic differentiation is based on the type of use and the function that the system will perform. The underlying technology or platform can be the same for both OLTP and OLAP systems, but the true differentiating factor is the logical function of the system itself.
If the system is continuously supporting the day-to-day requirements of the business (ATM’s, Checkout Systems, Order Processing, e-Commerce are all examples of OLTP systems.
OLAP systems are primarily the domain of business users who want to analyse the dynamics of the business in unique and innovative ways supported by the rich base of data that the organisation has accumulated through its OLTP systems. This can be analysis of customer usage patterns of the services offered by the company or it can be sales patterns of products based on marketing campaigns.
OLAP usage results in fewer transactions (database queries/transactions) but process huge volumes of data. OLTP systems typically process millions of transactions but process a microscopic amount of data.