May 2027
Intermediate to advanced
350 pages
13h 15m
English
Overview
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
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