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Universal Data Modeling
Universal Data Modeling
Knygos.lt klubas Knygos.lt nariams
105,56 €
-15%
Įprastai
124,19 €
  • Planuojame turėti už 95 d.
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 t…

Universal Data Modeling (el. knyga) (skaityta knyga) | Jun Shan | knygos.lt

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Aprašymas

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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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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