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Tag: data warehousing

Data Warehousing #5: Dimensional Design Process

Data Warehousing #5: Dimensional Design Process

This is the 5th in series of posts on datawarehousing.  To see the entire post list, click here. The Dimensional Design Process The four key decisions that are made when designing a Kimball-style dimensional star schema are as follows: Identify the Business Process Identify the Grain Identify the Dimensions Identify the Facts When embarking upon the design process, your final deliverable Read the article >>
Data Warehousing #4: Star Schemas

Data Warehousing #4: Star Schemas

This is the 4th in series of posts on datawarehousing.  To see the entire post list, click here. Star Schemas A star schema is the simplest type of data mart in dimensional modeling.  A star schema is one or more fact tables foreign key'd to any number of dimensions, and, when viewed through a visual schema planning tool, looks like a star: ---------------------------------------------------------------------------- |_______dim_user Read the article >>
Data Warehousing #3: Dimensions vs. Facts

Data Warehousing #3: Dimensions vs. Facts

This is the 3rd in series of posts on datawarehousing.  To see the entire post list, click here. Onto the schema design portion of the the series! There are two main types of tables used to store information in a data warehouse: Table Type #1: Dimension Table A dimension table contains the attributes by which users will query your data warehouse.  They are the content of the WHERE clause in Read the article >>
Data Warehousing #2: Data Flow

Data Warehousing #2: Data Flow

This is the 2nd in series of posts on datawarehousing.  To see the entire post list, click here. Independent Data Marts Like many others, I've worked in organizations which have evolved data marts like the one below: These systems are characterized by the necessity to query from multiple independent sources in order to meet a business objective.  Not only is this an inefficient way of processing Read the article >>
Data Warehousing For Fun & Profit

Data Warehousing For Fun & Profit

Coming from a world of B2C web applications, I am more than familiar with the reporting challenges associated with pulling analytics from transactional databases: (1) Profile information is often overwritten when UPDATEs are performed, (2) Information is segmented in different systems and worse, with different data access methods (If you want a headache, try writing an R script to mash up information Read the article >>