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Dr. Jugnesh Kumar, "Data Warehouse and Data Mining: Concepts, techniques and real life applications "
English | ISBN: 9355517343 | 2024 | 214 pages | MOBI | 2 MB
Unveiling insights, unleashing potential: Navigating the depths of data warehousing and mining for a data-driven tomorrow

Veronika Thir, "Data Protection and Information Privacy: Perceptions Across Cultures "
English | ISBN: 3111458121 | 2025 | 243 pages | EPUB | 19 MB
The impact of cultural factors on perceptions of and attitudes toward privacy is often neglected in privacy studies. Yet, understanding these factors is crucial in our globalized world, where businesses, governments and researchers rely on data from different cultures. This book makes a contribution to closing this gap. It presents and discusses findings from a large, comparative cross-cultural study on professionals' views of key data protection and information privacy issues, such as data autonomy, the data power of companies and governments, and the impact of data protection and information privacy regulations on companies, consumers, and the state. The book facilitates a better understanding of attitudes toward data protection and privacy across cultures by highlighting areas in which professionals around the world are (dis-)satisfied with data protection regulations and practices, and showing how culture-specific factors can help to explain differences in this area.

Theo Lynn, "Data Privacy and Trust in Cloud Computing: Building trust in the cloud through assurance and accountability "
English | ISBN: 3030546594 | 2021 | 170 pages | AZW3 | 673 KB
This open access book brings together perspectives from multiple disciplines including psychology, law, IS, and computer science on data privacy and trust in the cloud. Cloud technology has fueled rapid, dramatic technological change, enabling a level of connectivity that has never been seen before in human history. However, this brave new world comes with problems. Several high-profile cases over the last few years have demonstrated cloud computing's uneasy relationship with data security and trust.

Data Pipelines with Apache Airflow, Second Edition (MEAP V16)
English | 2025 | ISBN: 9781633436374 | 211 pages | MOBI | 4.23 Mb
Simplify, streamline, and scale your data operations with data pipelines built on Apache Airflow.

Data Modeling with Snowflake: A practical guide to accelerating Snowflake development using universal modeling techniques by Serge Gershkovich
English | September 2, 2025 | ISBN: 1837028036 | 444 pages | EPUB | 30 Mb
Learn to build scalable data architecture using Snowflake's unique features through real-world use cases and SQL recipes that unlock data value for business and engineering teams

Data Modeling with NoSQL Database: 3rd Edition by Ajit Singh
English | November 6, 2022 | ISBN: N/A | ASIN: B0BLLPHKPF | 104 pages | EPUB | 3.51 Mb
● An important step in database implementation is the data modeling, because it facilitates the understanding of the project through key features that can prevent programming and operation errors.

Data Marketing with BigQuery: The Non-Technical Guide to Analysis: From Excel to SQL: How to Master GA4, Escape Spreadsheet Hell, and Analyze Millions of Data Rows in Seconds
English | 18 Jan. 2026 | ASIN: B0GHHMCYNC | 81 pages | Epub | 348.07 KB
Is your spreadsheet crashing every time you open a monthly report? The era of the simple spreadsheet is over. Modern Digital Marketing generates millions of data points, and professionals who don't master SQL and Big Data risk becoming obsolete. But you don't need to be a software engineer to level up. This book is the survival guide for marketing analysts, media buyers, and growth hackers who need speed and scalability . Written in plain, non-technical language, it cuts through the jargon and focuses on what matters: business intelligence. What you will learn: How to connect Google Analytics 4 (GA4) to BigQuery for free. How to replace VLOOKUP with simple yet powerful SQL queries. How to "unnest" complex GA4 data structures with confidence. How to build automated, real-time dashboards in Looker Studio . Break free from the 1-million-row limit. Transform your career and become a full-stack Data Analyst today.

Data Lineage for AI: AI Data, Privacy & Transparency Series, #3
English | Jan 8, 2026 | ISBN: 9798232905057 | 197 pages | EPUB (True) | 143.08 KB
As artificial intelligence systems increasingly rely on large, complex, and externally sourced datasets, organizations are under growing pressure to prove where training data originated, how it was transformed, and whether it was used appropriately. Without defensible data lineage, AI systems become difficult to audit, explain, or regulate.

Data Lake Integration with Emerging Technologies by Ajit Singh
English | August 22, 2025 | ISBN: N/A | ASIN: B0FNK1VTQK | 294 pages | EPUB | 1.06 Mb
A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. But a lake, if left unattended, can quickly turn into a swamp-a chaotic and unusable mess of data. The true power of a data lake is unlocked only when it is integrated with powerful, transformative technologies that can navigate its depths, process its contents, and extract profound insights. This book, "Data Lake Integration with Emerging Technologies," is born from the necessity to bridge this critical gap.

Data Envelopment Analysis: An intuitive introduction to relative efficiency analysis
English | 13 Jan. 2026 | ASIN: B0GGGLHNFN | Pages not found | Epub | 426.98 KB
Efficiency is one of the most widely used - and most misunderstood - concepts in performance analysis. Most methods reduce efficiency to a single indicator, a ranking, or an average. Data Envelopment Analysis (DEA) takes a fundamentally different approach. This book offers a clear, intuitive, and conceptually rigorous introduction to DEA , designed for readers who want to understand efficiency - not just calculate it. Written by a researcher with extensive academic and applied experience in DEA, this book transforms a complex quantitative method into an accessible framework for reasoning about performance, comparison, and improvement. Rather than focusing on formulas or software, the book builds understanding step by step: What efficiency really means in real-world settings Why simple indicators and rankings often fail How relative efficiency differs from absolute performance What the efficient frontier represents - and what it does not Why flexibility in weights is essential for fairness How scale, orientation, benchmarks, and slacks shape interpretation When DEA should be used - and when it should not Throughout the book, efficiency is treated not as a judgment, but as a learning opportunity . DEA is presented as a tool for: benchmarking rather than ranking learning rather than labeling improvement rather than punishment The result is a book that bridges the gap between theory and practice, making DEA accessible to: undergraduate and graduate students researchers entering efficiency analysis professionals working with performance evaluation, benchmarking, or policy analysis If you have ever run a DEA model without fully trusting the results - or avoided the method because it seemed too technical - this book was written for you. Efficiency is not about being the best. It is about being the best possible, given the circumstances.