In today’s competitive business environment, data is an integral piece in establishing trends and correlations that can be used to transform Business Intelligence into Business Action. But efforts to effectively gather, analyze and act upon these insights have historically taken a great deal of time, personnel resources and money. In fact, according to Forrester, 80% of business intelligence efforts are spent on integrating business data with a platform of choice, yet 70% of the data produced in those efforts lack value or are completely irrelevant. That is hundreds of thousands of dollars and many months of work put in on something that ultimately doesn’t accomplish anything for the business!
Microsoft Power BI eliminates the guesswork, wasted time and energy in data integration and analysis. This user-friendly platform is designed to integrate business data from various sources and transform it into visualizations…
Recently I attempted to create an application that worked in an “Offline” mode. However one of the biggest hurdles I encountered was maintaining the data model throughout development. As most projects go, the definition of the database tables changed frequently, and maintaining the structure became a tangled mess of version numbers, alter statements and data migration scripts. In trying to solve for this problem and adhere to good practices, I created classes that stored my column names and indexes so I could later read them back from the cursor. Trying to add columns, though proved to be very code intensive.
After scrapping the idea due to a tight time line, I began researching other solutions in the JAVA/Android community. I came across GreenDAO ORM (http://greendao-orm.com/). It is a complete code generation which allows you to generate your data model without having to…
Conformed Dimensions and Facts
Data marts are often developed to represent important systems within a company. Over time additional data marts are added, and eventually there is a desire extract data across multiple marts. Extracting data across data marts can be cumbersome and some times impossible if the data marts were not designed to share common dimensions, also known as conformed dimensions. Ideally since the dimension criteria would be the same for each data mart, selecting data across multiple marts would be as routine as selecting data from a single data mart.
Going hand and hand with conformed dimensions, conformed facts involve standardizing facts across multiple marts. Adopting conformed facts eliminates the ambiguity of having facts that possess the same name but have different underlying calculations.
In this blog we use simple data marts to demonstrate the use of both conformed dimensions and…
Data warehousing is a big subject. This overview is intended to cover some of the most representative issues on a high level: the nature of OLAP systems, star schemas, facts and dimensions, and differing perspectives (Inmon vs. Kimball) on warehouse design.
OLTP vs. OLAP
OLTP systems are the operational databases supporting applications. They are highly normalized, and focused on CRUD operations.
OLAP databases are usually arranged in star schemas and are built for speed in retrieving aggregated data.