OLTP stands for On-line Transaction Processing and OLAP stands for On-line Analytical Processing. We may have heard these definition many times. But do we really understand the difference between them. Let's explore further on these two kinds of systems and their characteristic differences
A data warehouse is a repository of an organization's electronically stored data. Data warehouses are designed to facilitate reporting and analysis.
In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. He defined intelligence as: "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal."
Decision Support System (DSS) is a class of information systems (including but not limited to computerized systems) that support business and organizational decision-making activities. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, personal knowledge, or business models to identify and solve problems and make decisions.
This article discusses data warehousing from a holistic standpoint and quickly touches upon all the relevant concepts that one needs to know. Start here if you do not know where to start from.
Testing in data warehouse projects are till date a less explored area. However, if not done properly, this can be a major reason for data warehousing project failures - especially in user acceptance phase. Given here a mind-map that will help a project manager to think all the aspects of testing in data warehousing.
An enterprise data warehouse often fetches records from several disparate systems and store them centrally in an enterprise-wide warehouse. But what is the guarantee that the quality of data will not degrade in the process of centralization?
Data warehousing projects are one of its kinds. All data warehousing projects do not pose same challenges and not all of them are complex but they are always different. This article illustrates the top 5 challenges that often plague modern data warehousing developments. Knowing these challenges upfront is your best bet to avoid them.
This paper outlines some of the most important (and equally neglected) things that one must consider before and during the design phase of a data warehouse. In our experience, we have seen data warehouse designers often miss out on these items merely because they thought them to be too trivial to attract their attentions. Guess what, at the end of the day such neglects cost them heavily as they cut short the overall ROI of the data warehouse.
"Project Managers" are inevitable. Love them or hate them, but if you are in a project, you have to accept them. They are Omnipresent in any project. They intervene too much on technical things without much knowledge. They create unrealistic targets and nonsensical methods of achieving them. And they invariably fail to acknowledge the individual hard work. Are they of any use?
Watch this space...
By the typical definition of data warehouse, we expect the data warehouse to be non-volatile in nature for its entire design life time. As long as it remain operation, all data loaded in the data warehouse should remain there for the purpose of analysis. However, this is often not the case.