03014cam a2200457Ii 4500 ocn896901332 OCoLC 20190328114809.0 m o d cr ||||||||||| 141105t20142015ne o 000 0 eng d UKMGB eng rda pn UKMGB N$T VRC OPELS OCLCF TEFOD OCLCQ TMC MERER OCLCQ OCLCO OCLCA GILDS U3W D6H 016955847 Uk 016945767 Uk 9780128020449 (electronic bk.) 012802044X (electronic bk.) 9780128020913 (electronic bk.) 0128020911 (electronic bk.) (OCoLC)896901332 QA76.9.D37 COM 062000 bisacsh 005.745 23 Inmon, William H., author. Data architecture : a primer for the data scientist : big data, data warehouse and data vault / [electronic resource] William H. Inmon, Dan Linstedt. Amsterdam : Elsevier, [2014] �2015 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can't be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You'll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big DataUnderstand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data. Data warehousing. Big data. COMPUTERS Data Modeling & Design. bisacsh Big data. fast (OCoLC)fst01892965 Data warehousing. fast (OCoLC)fst00888026 Data Collection. Electronic books. Linstedt, Dan, author. ScienceDirect http://www.sciencedirect.com/science/book/9780128020449 247012 247012