Data architecture: a primer for the data scientist
Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There rem...
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| Main Authors: | , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
London, United Kingdom San Diego, CA
Academic Press
[2019]
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| Edition: | Second edition. |
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9780128169179/?ar |
| Summary: | Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. New case studies include expanded coverage of textual management and analytics New chapters on visualization and big data Discussion of new visualizations of the end-state architecture |
| Item Description: | Includes index. - Online resource; title from title page (Safari, viewed October 31, 2019) |
| Physical Description: | 1 Online-Ressource (1 volume) Illustrationen |
| ISBN: | 9780128169179 0128169176 |
Staff View
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| 520 | |a Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. New case studies include expanded coverage of textual management and analytics New chapters on visualization and big data Discussion of new visualizations of the end-state architecture | ||
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| spelling | Inmon, William H. VerfasserIn aut Data architecture a primer for the data scientist W.H. Inmon, Daniel Linstedt, Mary Levins Second edition. London, United Kingdom San Diego, CA Academic Press [2019] ©2019 1 Online-Ressource (1 volume) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes index. - Online resource; title from title page (Safari, viewed October 31, 2019) Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. New case studies include expanded coverage of textual management and analytics New chapters on visualization and big data Discussion of new visualizations of the end-state architecture Data warehousing Big data Electronic data processing Information retrieval Entrepôts de données (Informatique) Données volumineuses Recherche de l'information information retrieval Linstedt, Daniel VerfasserIn aut Levins, Mary VerfasserIn aut |
| spellingShingle | Inmon, William H. Linstedt, Daniel Levins, Mary Data architecture a primer for the data scientist Data warehousing Big data Electronic data processing Information retrieval Entrepôts de données (Informatique) Données volumineuses Recherche de l'information information retrieval |
| title | Data architecture a primer for the data scientist |
| title_auth | Data architecture a primer for the data scientist |
| title_exact_search | Data architecture a primer for the data scientist |
| title_full | Data architecture a primer for the data scientist W.H. Inmon, Daniel Linstedt, Mary Levins |
| title_fullStr | Data architecture a primer for the data scientist W.H. Inmon, Daniel Linstedt, Mary Levins |
| title_full_unstemmed | Data architecture a primer for the data scientist W.H. Inmon, Daniel Linstedt, Mary Levins |
| title_short | Data architecture |
| title_sort | data architecture a primer for the data scientist |
| title_sub | a primer for the data scientist |
| topic | Data warehousing Big data Electronic data processing Information retrieval Entrepôts de données (Informatique) Données volumineuses Recherche de l'information information retrieval |
| topic_facet | Data warehousing Big data Electronic data processing Information retrieval Entrepôts de données (Informatique) Données volumineuses Recherche de l'information information retrieval |
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