By Vincent Rainardi
I will inform - this writer has outfitted genuine information warehouses. This ebook has such a lot of genuine global program ideas, distilled into lower than 500 pages. I.E. it isn't no longer a bible booklet, that which places you to sleep, even though it is a smart reference publication. it's Inmon and Kimball agnostic - large profit the following. whereas the DW toolkit books are nice, they're merely nice for Kimball warehouses. Being within the Inmon camp, I delight in the authors' assurance of the innovations of the Operational facts shop, and Normalized information shop. This publication can be the 1st e-book you learn in development an information warehouse. even though the explicit sql code examples exhibit SQL Server code, all innovations may be utilized to Oracle, and so on. just a couple lawsuits. there's a loss of insurance for modeling localization, i.e. modeling for neighborhood language standards of the clients. the writer also needs to point out that information Modeling instruments, equivalent to ErWin, PowerDesigner (best IMO), or ER/Studio can fairly increase metadata modeling, and documentation, even supposing he covers metadata rather well. That apart, I nonetheless supply this publication a five superstar score, in lieu of the entire concept books in the market, which lack genuine international program examples.
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The OLTP system sends the changes to the MDM system and the MDM system stores the changes in the master data store, or the MDM system retrieves the master data in OLTP systems periodically to identify whether there are any changes. The first approach where the OLTP system sends the master data changes to the MDM system is called a push approach. The second approach where the MDM system retrieves the master data from the OLTP systems periodically is called a pull approach. Some MDM systems use the push approach, and some MDM systems use the pull approach.
Extract, transform, and load (ETL) is a system that has the capability to read the data from one data store, transform the data, and load it into another data store. The data store where the ETL reads the data from is called a source, and the data store that the ETL loads the data into is called a target. Figure 2-1 shows a data flow architecture with four data stores: stage, ODS, DDS, and MDB. Figure 2-1. A data flow architecture with a stage, ODS, DDS, and MDB The arrows in Figure 2-1 show the flows of data.
To do so, it must be able to perform a lot of database transactions (update, insert, delete, and select) in a relatively short period of time. In other words, it should be able to perform database transactions very quickly. If you stored the same piece of data—say, unit price—in many different places in the system, it would take a long time to update the data and to maintain data consistency. If you stored it in only one place, it would be quicker to update the data, and you wouldn’t have to worry about maintaining data consistency between different places.