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BiPM Encyclopedia →
Business Intelligence
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SECTION - Data-Warehouse/Mart
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This is one of the largest sections in this knowledge-base. It
covers on how you extract the information from various sources ,
put it all together, link it, clean it, transform it and then load
it in a place and form so that one can fulfill all the Data &
Information requirements of an organization. Data Warehousing can
also be named as "offline Integration" of data. This section
talks about a full-fledge Data Warehouse Initiative. However can
start small, while keeping in mind the long term road-map.
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Chapters
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This chapter is setting the scene. It provides the end to end high level landscape of Data Warehouse. Much of this chapter talks on how Data Warehouse is different from Transaction systems, and what are we in for. Lets Look at What & why of Data Warehouse, its components & framework and what are the challenges for a typical DW project.
Topics in this chapter : Data Warehouse definition- What is Data Warehouse? → Data Warehouse Purpose and Objective- Why is Data Warehouse Needed? → Data Warehouse Components and Framework → Data Warehouse Challenges and Issues →
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This phase is similar to any other project only till the basic principles. The workings and engagements of the project team is fairly different. DW projects fail when we try to apply conventional methods. Look at the journey which starts from finding a sponsor and ends with signing Project Agreement.
Topics in this chapter : Data Warehouse Project Initiation → Data Warehouse Project Readiness → Data Warehouse Project Scoping and Planning →
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Its time to get into details on the business requirements. Business requirements are less to do with detailing of business objectives, information needed and scorecards & dashboards. This phase is tough as it forces a clarity on business thinking.
Topics in this chapter : Data Warehouse Project plan → Data Warehouse Business Requirements → Data Warehouse Information Systems Assessment → Data Warehouse Requirements Assessment →
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Dimensional modeling is essentially a logical modeling of business requirements. It is different from data modeling. Lets look at what's dimensional modeling, why is it different?, why is it needed to be different?, what are special situations and how do we deal with them?.
Topics in this chapter : Data Warehouse Dimensional Model Components Concept → Dimensional Model Schemas- Star, Snow-Flake and Constellation → Dimensional Modeling vs. Relational Modeling → Foundation & Conformed Dimensions and Facts in Data Warehouse Dimensional Model → Slowly Changing Dimensions SCD in Dimensional Modeling →
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This chapter takes over from previous chapter, and details on how we go about converting the concepts into deliverables.
Topics in this chapter : Data Mart Business Theme Matrix in Data Warehouse Dimensional Model → Data Mart Dimension Fact table Matrix in Data Warehouse Dimensional Model → Data Mart Fact Table Grain Matrix in Data Warehouse Dimensional Model → Derived Facts table in Data Warehouse Dimensional Model → Derived Dimension Attributes Table → Dimensional Attributes+ Facts + Source System Matrix →
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This chapter deals with translating 'what' of business model to 'how' of Design & Architecture. Lets look at Extraction, transformation, loading, job control & audit, access services, quality assurance, infrastructure & lot more.
Topics in this chapter : Data Warehouse Design and Architecture Overview → Data Warehouse Source Systems → Data Warehouse ETL Extraction → Data Warehouse ETL Transformation → Data Warehouse ETL Loading → Data Warehouse Metadata → Back-Room Data Warehouse Metadata → Data Warehouse Data Quality assurance → Data Warehouse job control and audit → Data Warehouse sharing and browsing → Data Warehouse Infrastructure →
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Like some other aspects, Data warehouse testing is fairly different from a transaction processing system. The amount of data and possible test scenarios can run into huge sets. The trick is to find a right balance.
Topics in this chapter : Data Warehouse Testing is Different → Data Warehouse Testing Categories → Data Warehouse Test Scenarios → Data Warehouse Test Data → Data Warehouse Implementation Deployment →
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Challenges are two ways. If user gets engaged, the expectations rise, and any failure to provide adequate performance levels or to add enhancements lead to rejections. On the other hand, user may also be resistant to come out of routine reporting & analysis methods. This chapter look at the addressal of these challenges.
Topics in this chapter : Data Warehouse Benefits Usage → Data Warehouse Performance Management and operations → Data Warehouse Change and Enhancement Management →
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We have covered many diverse topics in Data Warehouse Section. This chapter is dedicated to bring them all into a list and sequence of activities that you will do to make a data warehouse project happen. We have covered different data warehouse topics at various stages. This chapter puts them all together in an integrated fashion. Though most of the activities here are linked to the data warehouse, may of them are linked to the overall BI including OLAP and end-user tools. One has to note that Data Warehouse initiative is not implemented alone. It is always associated with the implementation of the end-user tools (like query tools, data mining tools, reporting tools...). We will have one topic page assigned to each major phase of a Data Warehouse project.
Topics in this chapter : Data Warehouse Project Definition → Data Warehouse Project Initiation Phase → Data Warehouse Business Requirements Gathering Phase → Data Warehouse Modeling and Analyze Phase → Data Warehouse Design Phase → OLAP + Data Warehouse Design Phase → Physical Database Design and Implementation →
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