Most data professionals work with multiple datasets scattered across teams, systems, and formats. But without a clear modeling strategy, the result is often chaos: mismatched schemas, fragile pipelines, and a constant fight to make sense of the noise. This essential guide offers a better way by introducing a practical framework for designing high-quality data models that work across platforms while supporting the growing demands of AI, analytics, and real-time systems. Author Jun Shan bridges the gap between disconnected modeling approaches and the need for a unified, system-agnostic methodology. Whether you're building a new data platform or rethinking legacy infrastructure, Universal Data Modeling gives you the clarity, patterns, and tools to model data that's consistent, resilient, and ready to scale. Connect conceptual, logical, and physical modeling phases with confidence Apply best-fit techniques across relational, semistructured, and NoSQL formats Improve data quality, clarity, and maintainability across your organization Support modern design paradigms like data mesh and data products Translate domain knowledge into models that empower teams Build flexible, scalable models that stand the test of technology change

Dive into the world of data analytics with 'SQL for Data Analytics'. This book takes you beyond simple query writing to teach you how to use SQL to analyze, interpret, and derive actionable insights from real-world data. By the end, you'll build technical skills that allow you to solve complex problems and demonstrate results using data. What this Book will help me do Understand how to create, manage, and utilize structured databases for analytics. Use advanced SQL techniques such as window functions and subqueries effectively. Analyze various types of data like geospatial, JSON, and time-series data in SQL. Apply statistical principles within the context of SQL for enhanced insights. Automate data workflows and presentations using SQL and Python integration. Author(s) The authors Jun Shan, Haibin Li, Matt Goldwasser, Upom Malik, and Benjamin Johnston bring together a wealth of knowledge in data analytics, database management, and applied statistics. Together, they aim to empower readers through clear explanations, practical examples, and a focus on real-world applicability. Who is it for? This book is aimed at data professionals and learners such as aspiring data analysts, backend developers, and anyone involved in data-driven decision-making processes. The ideal reader has a basic understanding of SQL and mathematics and is eager to extend their skills to tackle real-world data challenges effectively.

SQL for Data Analytics is an accessible guide to helping readers efficiently use SQL for data analytics tasks. You will learn the ins and outs of writing SQL queries, preparing datasets, and utilizing advanced features like geospatial data handling and window functions. Demystify the process of harnessing SQL to tackle analytical data challenges in a structured and hands-on way. What this Book will help me do Become proficient in preparing and managing datasets using SQL. Learn to write efficient SQL queries for summarizing and analyzing data. Master advanced SQL features, including window functions and JSON handling. Optimize SQL queries and automate analytical tasks for efficiency. Gain practical experience analyzing data with real-world scenarios. Author(s) The authors, Jun Shan, Matt Goldwasser, Upom Malik, and Benjamin Johnston, are experienced professionals in data analytics and database management. They bring a blend of technical expertise and practical insights to teaching SQL for analytics. Their collective knowledge ensures that the book caters to all levels, from foundational concepts to advanced techniques. Who is it for? This book is ideal for database engineers transitioning into analytics, backend engineers looking to deepen their understanding of production data, and data scientists or business analysts seeking to boost their SQL analytics skills. Readers should have a basic grasp of SQL and familiarity with statistics and linear algebra to fully benefit from the contents.