By Vincent Rainardi
Building a knowledge Warehouse: With Examples in SQL Server describes tips on how to construct a knowledge warehouse thoroughly from scratch and indicates useful examples on the way to do it. writer Vincent Rainardi additionally describes a few useful matters he has skilled that builders are inclined to come across of their first facts warehousing undertaking, in addition to strategies and recommendation. The relational database administration process (RDBMS) utilized in the examples is SQL Server; the model aren't a topic so long as the person has SQL Server 2005 or later.
The ebook is geared up as follows. at the beginning of this ebook (chapters 1 via 6), you the right way to construct an information warehouse, for instance, defining the structure, figuring out the technique, accumulating the necessities, designing the information versions, and developing the databases. Then in chapters 7 via 10, you methods to populate the information warehouse, for instance, extracting from resource platforms, loading the knowledge shops, preserving information caliber, and using the metadata. once you populate the knowledge warehouse, in chapters eleven via 15, you discover the right way to current facts to clients utilizing studies and multidimensional databases and the way to exploit the knowledge within the information warehouse for enterprise intelligence, buyer courting administration, and different reasons. Chapters sixteen and 17 wrap up the e-book: upon getting outfitted your info warehouse, prior to it may be published to construction, you must try out it completely. After your program is in construction, you must know how to manage facts warehouse operation.
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Additional resources for Building a Data Warehouse: With Examples in SQL Server
If the information in the data warehouse is huge, often it is easier to search than to browse. For example, a top international organization with 100 branches located in 50 countries could have 100 data marts totaling 100 terabytes for sales, purchases, finance, inventory, human resources, and a huge amount of documents. This is especially true for new users who have not learned the structure of the data warehouse, what reports are available, and how to navigate around the data using the OLAP interface.
A surrogate key is the identifier of the master data row within the data warehouse. In the DDS, the surrogate key is used as the primary key of dimension tables. The surrogate key is a sequential integer, starting from 0. So, it is 0, 1, 2, 3, …, and so on. Using the surrogate key, you can identify a unique record on a dimension table. Surrogate keys also exist in the fact tables to identify the dimension attributes for a particular business transaction. Surrogate keys are used to link a fact table and the dimension tables.
Second, it is able to load data into several DDSs. Unlike the single DDS architecture, in the NDS + DDS architecture you can have several DDSs. Figure 2-5 shows the NDS + DDS data flow architecture. Figure 2-5. NDS + DDS data flow architecture In the NDS + DDS architecture, NDS is the master data store, meaning NDS contains the complete data sets, including all historical transaction data and all historical versions of master data. Historical transaction data means the business transactions that happened in the 35 36 CHAPTER 2 ■ DATA WAREHOUSE ARCHITECTURE past.
Building a Data Warehouse: With Examples in SQL Server by Vincent Rainardi