Date and time-stamped dimensions are dimensions that track the history of changes in the dimension tables themselves. (Star Schema, 2012) The easiest way to think about these type of dimensions are regular data dimensions that have the added columns necessary to show each record by a specific date or time. […]
As we slowly move towards a more data focused society as well as with companies well entrenched in the usage of data, analytics, and business intelligence we see that analysts that are hyper-focused in their area of expertise and job function are increasingly more capable with the nitty-gritty details. However, you’ll see that the […]
For some this will be a no-brainer and for those I apologize, however for a good majority of you I think this is going to be a good post to remind you that any report you are building whether in Cognos, SAP, MicroStrategy, Microsoft, or what have you, strive to always do […]
A Data warehouse is a repository of an organization’s electronically stored data. Data warehouses are designed to manage and store the data whereas the Business Intelligence (BI) focuses on the usage of data to facilitate reporting and analysis. The purpose of a data warehouse is to house standardized, structured,
There are three types of slowly changing dimensions: Type 1, Type 2, and Type 3. Each of these types tries to help the designer of the star schema eliminate paradox from their dimensional model (just as the three interpretations of the Schrödinger’s Cat thought experiment tries to eliminate the paradox of the living dead).
The reporting, analysis, and interpretation of business data is of central importance to a company in guaranteeing its competitive edge, optimizing processes, and enabling it to react quickly and in line with the market.
There is a quite a bit of misinformation in the world of Dimensional Modeling on what certain things are and what they aren’t and Fact Tables are often misunderstood. The term “Fact” in relation to Data Warehousing/Dimensional Modeling really started from a joint research project conducted by General Mills and Dartmouth University the 1960’s
Ralph Kimball is an author on the subject of data warehousing and business intelligence. He is widely regarded as one of the original architects of data warehousing and is known for long-term convictions that data warehouses must be designed to be understandable and fast. His methodology, also known as dimensional modeling or the Kimball […]
The Data Warehouse Lifecycle Toolkit
The Kimball Lifecycle provides the overall framework that ties together the various activities of a Data Warehouse/Business Intelligence implementation. The Kimball Lifecycle is thoroughly documented and explained in the book “The Data Warehouse Lifecycle Toolkit” by Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, and Bob Becker.
In Ralph Kimball’s book “The Data Warehouse Toolkit” he describes the basic elements of a data warehouse. There are essentially four main components: Operational Source Systems, Data Staging Area, Data Presentation Area, and Data Access Tools. Although Operational Source Systems is part of the model, Kimball states “The source systems should really be […]
Please follow us :)5k
- Analytics (21)
- Big Data (9)
- Business Intelligence (59)
- Data Science (70)
- Miscellaneous (17)
Tags2008 Analysis Analytics Article Big Data Book Business Intelligence Charts Cognos Dashboards Data Data Visualization Data Warehouse Design Dimensional Fusion Tables Google Hadoop Humor IBM Logical Market Microsoft Model Modeling Operational Predictive Programming Python R Ralph Kimball Reporting Science Server SQL SQL Server SSIS Statistics TED Tools Tutorial Unstructured Video Visualization Warehousing