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ISBN 10: 8868807084 ISBN 13: 9788868807085
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Añadir al carritoCondición: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.
Publicado por Springer, Berlin|Apress, 2024
ISBN 10: 1484297342 ISBN 13: 9781484297346
Idioma: Inglés
Librería: moluna, Greven, Alemania
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Publicado por Apress, Apress Okt 2023, 2023
ISBN 10: 1484297342 ISBN 13: 9781484297346
Idioma: Inglés
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware -This book explains how to implement a data lake strategy, covering the technical and business challenges architects commonly face. It also illustrates how and why client requirements should drive architectural decisions.Drawing upon a specific case from his own experience, author Nayanjyoti Paul begins with the consideration from which all subsequent decisions should flow: what does your customer need He also describes the importance of identifying key stakeholders and the key points to focus on when starting a new project. Next, he takes you through the business and technical requirement-gathering process, and how to translate customer expectations into tangible technical goals. From there, yoüll gain insight into the security model that will allow you to establish security and legal guardrails, as well as different aspects of security from the end user¿s perspective. Yoüll learn which organizational roles need to be onboarded into the data lake, their responsibilities, the services they need access to, and how the hierarchy of escalations should work. Subsequent chapters explore how to divide your data lakes into zones, organize data for security and access, manage data sensitivity, and techniques used for data obfuscation. Audit and logging capabilities in the data lake are also covered before a deep dive into designing data lakes to handle multiple kinds and file formats and access patterns. The book concludes by focusing on production operationalization and solutions to implement a production setup.After completing this book, you will understand how to implement a data lake, the best practices to employ while doing so, and will be armed with practical tips to solve business problems.What You Will LearnUnderstand the challenges associated with implementing a data lakeExplore the architectural patterns and processes used to design a new data lakeDesign and implement data lake capabilitiesAssociate business requirements with technical deliverables to drive successWho This Book Is ForData Scientists and Architects, Machine Learning Engineers, and Software Engineers.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 224 pp. Englisch.
Librería: preigu, Osnabrück, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Practical Implementation of a Data Lake | Translating Customer Expectations into Tangible Technical Goals | Nayanjyoti Paul | Taschenbuch | xx | Englisch | 2023 | Apress | EAN 9781484297346 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explains how to implement a data lake strategy, covering the technical and business challenges architects commonly face. It also illustrates how and why client requirements should drive architectural decisions.Drawing upon a specific case from his own experience, author Nayanjyoti Paul begins with the consideration from which all subsequent decisions should flow: what does your customer need He also describes the importance of identifying key stakeholders and the key points to focus on when starting a new project. Next, he takes you through the business and technical requirement-gathering process, and how to translate customer expectations into tangible technical goals. From there, you'll gain insight into the security model that will allow you to establish security and legal guardrails, as well as different aspects of security from the end user's perspective. You'll learn which organizational roles need to be onboarded into the data lake, their responsibilities, the services they need access to, and how the hierarchy of escalations should work. Subsequent chapters explore how to divide your data lakes into zones, organize data for security and access, manage data sensitivity, and techniques used for data obfuscation. Audit and logging capabilities in the data lake are also covered before a deep dive into designing data lakes to handle multiple kinds and file formats and access patterns. The book concludes by focusing on production operationalization and solutions to implement a production setup.After completing this book, you will understand how to implement a data lake, the best practices to employ while doing so, and will be armed with practical tips to solve business problems.What You Will LearnUnderstand the challenges associated with implementing a data lakeExplore the architectural patterns and processes used to design a new data lakeDesign and implement data lake capabilitiesAssociate business requirements with technical deliverables to drive successWho This Book Is ForData Scientists and Architects, Machine Learning Engineers, and Software Engineers. 224 pp. Englisch.
Librería: Majestic Books, Hounslow, Reino Unido
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Añadir al carritoCondición: New. Print on Demand pp. 224.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Añadir al carritoCondición: New. PRINT ON DEMAND pp. 224.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 31,69
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book explains how to implement a data lake strategy, covering the technical and business challenges architects commonly face. It also illustrates how and why client requirements should drive architectural decisions.Drawing upon a specific case from his own experience, author Nayanjyoti Paul begins with the consideration from which all subsequent decisions should flow: what does your customer need He also describes the importance of identifying key stakeholders and the key points to focus on when starting a new project. Next, he takes you through the business and technical requirement-gathering process, and how to translate customer expectations into tangible technical goals. From there, you'll gain insight into the security model that will allow you to establish security and legal guardrails, as well as different aspects of security from the end user's perspective. You'll learn which organizational roles need to be onboarded into the data lake, their responsibilities, the services they need access to, and how the hierarchy of escalations should work. Subsequent chapters explore how to divide your data lakes into zones, organize data for security and access, manage data sensitivity, and techniques used for data obfuscation. Audit and logging capabilities in the data lake are also covered before a deep dive into designing data lakes to handle multiple kinds and file formats and access patterns. The book concludes by focusing on production operationalization and solutions to implement a production setup.After completing this book, you will understand how to implement a data lake, the best practices to employ while doing so, and will be armed with practical tips to solve business problems.What You Will LearnUnderstand the challenges associated with implementing a data lakeExplore the architectural patterns and processes used to design a new data lakeDesign and implement data lake capabilitiesAssociate business requirements with technical deliverables to drive successWho This Book Is ForData Scientists and Architects, Machine Learning Engineers, and Software Engineers.