May 1, 2024
Updated May 9, 2025
28 minute read
Data modeling is the process of creating a visual representation, or blueprint, of an information system or parts of it. This blueprint helps to define and organize the different types of data a business collects and produces, and it clarifies the relationships between these data points. Think of it as an architect's plan for a building; it outlines the structure, how different parts connect, and ensures everything works together efficiently. Data models are built around specific business needs, with rules and requirements defined upfront by business stakeholders to ensure the final system meets their objectives.
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Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Modeling.
Provides a beginner-friendly introduction to data modeling, covering fundamental concepts, techniques, and diagramming. It includes hands-on exercises and self-tests to reinforce learning, making it suitable for high school and undergraduate students, as well as those new to the field.
The first volume in a series, this book offers a collection of universal data models applicable across various industries. It's a practical guide providing pre-built patterns for common business concepts like parties, products, and orders. This is an excellent reference for data modelers at all levels, particularly useful for jump-starting modeling projects.
While not solely focused on data modeling, this book provides a comprehensive overview of the systems and concepts underlying modern data management. It discusses various data models in the context of distributed systems, scalability, and reliability, offering valuable insights for architects and engineers.
Offers a practical, step-by-step guide to relational database design, including data modeling principles. It's aimed at beginners and those without extensive technical backgrounds, making it suitable for high school or early undergraduate students and business professionals who need to understand database fundamentals.
Explores reusable data model patterns for common business structures. It helps in applying data modeling rules in an enterprise context and provides high-level models for various business areas. This valuable resource for experienced modelers and professionals looking for proven solutions to recurring modeling challenges.
The third book in the series delves deeper into universal data modeling patterns, offering more advanced and complex patterns. It's suitable for experienced data modelers looking to expand their pattern library and tackle more intricate modeling scenarios.
Focuses on the principles and practices for developing high-quality data models. It emphasizes the importance of data model quality and provides techniques for achieving it throughout the modeling process. It valuable resource for data modelers seeking to improve their craft and build robust, maintainable models.
Focuses on aligning data modeling with business needs and strategy. It emphasizes the importance of involving business stakeholders in the modeling process and provides techniques for creating high-level data models that have significant business impact. It's particularly relevant for business analysts and data professionals working closely with business teams.
Introduces agile methodologies for data warehousing and dimensional modeling, focusing on collaborative techniques like 'modelstorming'. It's relevant for teams adopting agile practices in their data projects and provides a practical approach to dimensional modeling.
Presents a user-driven approach to conceptual and logical data modeling, emphasizing collaboration with business stakeholders. It introduces techniques for building and refining data models based on user requirements. It valuable resource for both modelers and business analysts involved in data projects.
Delves into the theoretical and practical aspects of data modeling, including research on how practitioners approach the discipline. It's suitable for advanced students, researchers, and experienced professionals interested in the nuances and complexities of data modeling.
Considered the "father of the data warehouse," Bill Inmon's book classic that defines the data warehouse and its architecture. While pre-dating some modern approaches, it provides foundational knowledge on the principles of data warehousing that influence data modeling for analytical systems.
Focuses on data modeling within the SAP HANA platform, covering techniques for building predictive, simulation, and optimization models. It's a specialized resource for professionals working with SAP HANA and provides practical guidance for advanced modeling scenarios in that environment.
Delves into the foundations and applications of conceptual modeling, which is the initial stage of data modeling. It explores various conceptual modeling techniques and their use in capturing business requirements. It's a good resource for students and researchers interested in the theoretical underpinnings of data modeling. While not a recent publication, the foundational concepts remain relevant.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/kl3zfw/data
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