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2024 (4)

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Book
Data Stewardship in Action : A Roadmap to Data Value Realization and Measurable Business Outcomes
Authors: ---
ISBN: 1837638128 Year: 2024 Publisher: Birmingham, England : Packt Publishing Ltd.,

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Take your organization's data maturity to the next level by operationalizing data governance Key Features Develop the mindset and skills essential for successful data stewardship Apply practical advice and industry best practices, spanning data governance, quality management, and compliance, to enhance data stewardship Follow a step-by-step program to develop a data operating model and implement data stewardship effectively Purchase of the print or Kindle book includes a free PDF eBook Book Description In the competitive data-centric world, mastering data stewardship is not just a requirement--it's the key to organizational success. Unlock strategic excellence with Data Stewardship in Action, your guide to exploring the intricacies of data stewardship and its implementation for maximum efficiency. From business strategy to data strategy, and then to data stewardship, this book shows you how to strategically deploy your workforce, processes, and technology for efficient data processing. You'll gain mastery over the fundamentals of data stewardship, from understanding the different roles and responsibilities to implementing best practices for data governance. You'll elevate your data management skills by exploring the technologies and tools for effective data handling. As you progress through the chapters, you'll realize that this book not only helps you develop the foundational skills to become a successful data steward but also introduces innovative approaches, including leveraging AI and GPT, for enhanced data stewardship. By the end of this book, you'll be able to build a robust data governance framework by developing policies and procedures, establishing a dedicated data governance team, and creating a data governance roadmap that ensures your organization thrives in the dynamic landscape of data management. What you will learn Enhance your job prospects by understanding the data stewardship field, roles, and responsibilities Discover how to develop a data strategy and translate it into a functional data operating model Develop an effective and efficient data stewardship program Gain practical experience of establishing a data stewardship initiative Implement purposeful governance with measurable ROI Prioritize data use cases with the value and effort matrix Who this book is for This book is for professionals working in the field of data management, including business analysts, data scientists, and data engineers looking to gain a deeper understanding of the data steward role. Senior executives who want to (re)establish the data governance body in their organizations will find this resource invaluable. While accessible to both beginners and professionals, basic knowledge of data management concepts, such as data modeling, data warehousing, and data quality, is a must to get started.


Book
Azure Data Factory by Example : Practical Implementation for Data Engineers
Author:
ISBN: 9798868802188 Year: 2024 Publisher: Berkeley, CA : Apress : Imprint: Apress,

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Data engineers who need to hit the ground running will use this book to build skills in Azure Data Factory v2 (ADF). The tutorial-first approach to ADF taken in this book gets you working from the first chapter, explaining key ideas naturally as you encounter them. From creating your first data factory to building complex, metadata-driven nested pipelines, the book guides you through essential concepts in Microsoft’s cloud-based ETL/ELT platform. It introduces components indispensable for the movement and transformation of data in the cloud. Then it demonstrates the tools necessary to orchestrate, monitor, and manage those components. This edition, updated for 2024, includes the latest developments to the Azure Data Factory service: Enhancements to existing pipeline activities such as Execute Pipeline, along with the introduction of new activities such as Script, and activities designed specifically to interact with Azure Synapse Analytics. Improvements to flow control provided by activity deactivation and the Fail activity. The introduction of reusable data flow components such as user-defined functions and flowlets. Extensions to integration runtime capabilities including Managed VNet support. The ability to trigger pipelines in response to custom events. Tools for implementing boilerplate processes such as change data capture and metadata-driven data copying.


Dissertation
Enhancing Warehousing Education through Serious Games
Authors: --- ---
Year: 2024 Publisher: Liège Université de Liège (ULiège)

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The rapid advancement of technology has revolutionized education, offering innovative tools that enhance student engagement and learning outcomes. This study explores the integration of a serious game, developed by PhD student Sarah Saufney, into the teaching of warehousing concepts at HEC Liège. Traditional pedagogical approaches often struggle to engage students in supply chain management courses, particularly in warehousing, where practical knowledge is crucial. This research evaluates the effectiveness of the serious game in improving learning assessment, student engagement, and motivation, compared to traditional methods.
Guided principally by the Technology Acceptance Model (TAM), this study conducted a quantitative analysis through pre-test and post-test surveys with 30 students. The findings reveal significant improvements in knowledge assessment, perceived usefulness, ease of use, and student motivation after using the serious game. The game's interactive and immersive nature not only facilitated understanding of complex warehousing tasks but also fostered higher levels of student interest and confidence.
The study underscores the potential of serious games as a pedagogical tool, suggesting their broader application in supply chain management education. It also highlights the need for further research to refine these tools and explore their long-term impact on student preparedness for professional challenges. By addressing the limitations identified, such as sample size and game complexity, future studies could enhance the generalizability and effectiveness of serious games in academic settings.


Book
Architecting a Modern Data Warehouse for Large Enterprises : Build Multi-cloud Modern Distributed Data Warehouses with Azure and AWS
Authors: --- ---
ISBN: 9798868800290 Year: 2024 Publisher: Berkeley, CA : Apress : Imprint: Apress,

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Design and architect new generation cloud-based data warehouses using Azure and AWS. This book provides an in-depth understanding of how to build modern cloud-native data warehouses, as well as their history and evolution. The book starts by covering foundational data warehouse concepts, and introduces modern features such as distributed processing, big data storage, data streaming, and processing data on the cloud. You will gain an understanding of the synergy, relevance, and usage data warehousing standard practices in the modern world of distributed data processing. The authors walk you through the essential concepts of Data Mesh, Data Lake, Lakehouse, and Delta Lake. And they demonstrate the services and offerings available on Azure and AWS that deal with data orchestration, data democratization, data governance, data security, and business intelligence. After completing this book, you will be ready to design and architect enterprise-grade, cloud-based modern data warehouses using industry best practices and guidelines. You will: Understand the core concepts underlying modern data warehouses Design and build cloud-native data warehouses Gain a practical approach to architecting and building data warehouses on Azure and AWS Implement modern data warehousing components such as Data Mesh, Data Lake, Delta Lake, and Lakehouse Process data through pandas and evaluate your model’s performance using metrics such as F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications.

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