{"product_id":"practical-implementation-of-a-data-lake-translating-customer-expectations-into-tangible-technical-goals-paperback","title":"Practical Implementation of a Data Lake: Translating Customer Expectations Into Tangible Technical Goals - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eNayanjyoti Paul\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eDrawing 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.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eAfter 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.\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUnderstand the challenges associated with implementing a data lake\u003c\/li\u003e\n\u003cli\u003eExplore the architectural patterns and processes used to design a new data lake\u003c\/li\u003e\n\u003cli\u003eDesign and implement data lake capabilities\u003c\/li\u003e\n\u003cli\u003eAssociate business requirements with technical deliverables to drive success\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Scientists and Architects, Machine Learning Engineers, and Software Engineers.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eDrawing 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.\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003eAfter 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.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eYou will: \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUnderstand the challenges associated with implementing a data lake\u003c\/li\u003e\n\u003cli\u003eExplore the architectural patterns and processes used to design a new data lake\u003c\/li\u003e\n\u003cli\u003eDesign and implement data lake capabilities\u003c\/li\u003e\n\u003cli\u003eAssociate business requirements with technical deliverables to drive success\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eNayanjyoti Paul\u003c\/b\u003e is an Associate Director and Chief Azure Architect for GenAI and LLM CoE for Accenture. He is the product owner and creator of a patented asset. Presently, he leads multiple projects as a lead architect around generative AI, large language models, data analytics, and machine learning. Nayan is a certified Master Technology Architect, certified Data Scientist, and certified Databricks Champion with additional AWS and Azure certifications. He is a speaker at conferences like Strata Conference, Data Works Summit, and AWS Reinvent. He also delivers guest lectures at Universities.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 202\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.47 x 9.21 x 6.14 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e October 04, 2023\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":43155754254399,"sku":"9781484297346","price":38.86,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/OiPhnojFjh9781484297346.webp?v=1776966274","url":"https:\/\/dhlswag.com\/products\/practical-implementation-of-a-data-lake-translating-customer-expectations-into-tangible-technical-goals-paperback","provider":"BBB","version":"1.0","type":"link"}