Data architecture : a primer for the data scientist : big data, data warehouse and data vault /
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)....
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| Main Authors: | , |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Amsterdam :
Elsevier,
[2014]
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| Subjects: | |
| Online Access: | CONNECT |
MARC
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| 245 | 1 | 0 | |a Data architecture : |b a primer for the data scientist : big data, data warehouse and data vault / |c William H. Inmon, Dan Linstedt. |
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| 520 | |a 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. | ||
| 505 | 0 | |a Corporate data -- The data infrastructure -- The "great divide" -- Demographics of corporate data -- Corporate data analysis -- The life cycle of data : understanding data over time -- A brief history of data -- A brief history of big data -- What is big data? -- Parallel processing -- Unstructured data -- Contextualizing repetitive unstructured data -- Textual disambiguation -- Taxonomies -- A brief history of data warehouse -- Integrated corporate data -- Historical data -- Data marts -- What a data warehouse is not -- Introduction to data vault -- Introduction to data vault modeling -- Introduction to data vault architecture -- Introduction to data vault methodology -- Introduction to data vault implementation -- The operational environment : a short history -- The standard work unit -- Data modeling for the structured environment -- Metadata -- Data governance of structured data -- A brief history of data architecture -- Big data/existing systems interface -- The data warehouse/operational environment interface -- Data architecture : a high-level perspective -- Repetitive analytics : some basics -- Analyzing repetitive data -- Repetitive analysis -- Nonrepetitive data -- Mapping -- Analytics from nonrepetitive data -- Operational analytics -- Operational analytics -- Personal analytics -- A composite data architecture. | |
| 500 | |a ScienceDirect eBook - Computer Science 2015 |5 TMurS | ||
| 650 | 0 | |a Data warehousing. | |
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Statistics |x Methodology. | |
| 700 | 1 | |a Linstedt, Dan, |e author. | |
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