Practical Implementation of a Data Lake - by Nayanjyoti Paul (Paperback)

Book Synopsis

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 Learn

• Understand the challenges associated with implementing a data lake
• Explore the architectural patterns and processes used to design a new data lake
• Design and implement data lake capabilities
• Associate business requirements with technical deliverables to drive success

Who This Book Is For

Data Scientists and Architects, Machine Learning Engineers, and Software Engineers.

From the Back Cover

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.

You will:

• Understand the challenges associated with implementing a data lake
• Explore the architectural patterns and processes used to design a new data lake
• Design and implement data lake capabilities
• Associate business requirements with technical deliverables to drive success

Review Quotes

"The content of this book is often structured as tables, or as overly extensive bullet lists, and includes numerous illustrations. Well-meant suborning of the original, informal intent may result in producing skeleton checklists ... . The book is explicitly aimed at data scientists and architects, machine learning engineers, and software engineers. Overall, the information provided, once the reader masters its presentation, is valuable for project-leadership-minded individuals ... ." (A. Squassabia, Computing Reviews, February 26, 2024)

About the Author

Nayanjyoti Paul 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.