This paper discusses the natural characteristics of data in general. Understanding these characteristics help you classify the data appropriately while doing data modeling or data mining. This tutorial is written for the data modelers and data miners.
In this multi part tutorial we will learn the basics of dimensional modeling and we will see how to use this modeling technique in real life scenario. At the end of this tutorial you will become a confident dimensional data modeler.
Now that we know the basic approach to do dimensional modeling from our earlier article, let us spend some time to understand various possible schema in dimensional modeling.
In our earlier article we have seen how to design a simple dimensional data model for a point-of-sale system (as an example we took the case of McDonald's fast-food shop). In this article we will begin with the same model and we will see how we may enhance the model to store historical changes in the attributes of dimension table.
In our earlier article we have discussed the need of storing historical information in dimensional tables. We have also learnt about various types of changing dimensions. In this article we will pick "slowly changing dimension" only and learn in detail about various types of slowly changing dimensions and how to design them.
Handling rapidly changing dimensions are tricky due to various performance implications. This article attempts to provide some methodologies on handling rapidly changing dimensions in a data warehouse.
A model is an abstraction of some aspect of a problem. A data model is a model that describes how data is represented and accessed, usually for a database. The construction of a data model is one of the most difficult tasks of software engineering and is often pivotal to the success or failure of a project.
Performance of a data warehouse is as important as the correctness of data in the data warehouse because unacceptable performance may render the data warehouse as useless. There is this increasing awareness about the fact that it’s much effective to build the performance from the beginning rather than to tune the performance at the end. In this article we have a few points that you may consider for optimally building the data model of a data warehouse. We will only consider performance considerations for dimensional modeling.
This article attempts to explain the rudimentary concepts of data warehousing in the form of typical data warehousing interview questions along with their standard answers. After reading this article, you should gain good amount of knowledge on various concepts of data warehousing.
This article is the continuation of the article "Top 50 DWBI Interview Questions with Answers"
Continuation to our collection of Data Warehouse Conceptual Questions.