dbt for Analytics Engineering (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"dbt for Analytics Engineering" "dbt for Analytics Engineering" is a comprehensive guide for modern data practitioners seeking to master the evolving discipline of analytics engineering. The book begins by tracing the origins of analytics engineering and examining the emergence of the modern data stack, with an in-depth look at dbt's transformative role in shaping data workflows, architectural patterns, and large-scale organizational adoption. Through real-world case studies and expert insights, readers will gain a foundational understanding of how dbt enables efficient, collaborative, and sca...
"dbt for Analytics Engineering"
"dbt for Analytics Engineering" is a comprehensive guide for modern data practitioners seeking to master the evolving discipline of analytics engineering. The book begins by tracing the origins of analytics engineering and examining the emergence of the modern data stack, with an in-depth look at dbt's transformative role in shaping data workflows, architectural patterns, and large-scale organizational adoption. Through real-world case studies and expert insights, readers will gain a foundational understanding of how dbt enables efficient, collaborative, and scalable data transformation practices within diverse business contexts.
Diving into advanced project architecture, the book offers practical frameworks for structuring scalable dbt projects, managing configurations across multiple environments, and implementing robust model materializations. Readers will learn to harness Jinja and macros for code reusability, ensure high-performance data modeling using dimensional and Data Vault approaches, and adopt modular design patterns that optimize both maintainability and analytical clarity. In addition, dedicated chapters address the rigorous testing, quality assurance, and data governance practices needed to ensure trust, compliance, and discoverability in enterprise data assets.
The practical reach of "dbt for Analytics Engineering" extends to cloud warehouse optimization, orchestration, automation, and CI/CD integration, providing readers with strategies for deploying and managing analytics projects at enterprise scale. The book concludes by exploring the technological frontiers of analytics engineering-from integrating machine learning and real-time data streaming to building custom dbt plugins and embracing federated data models. With actionable guidance on scaling analytics teams, managing dependencies, and implementing secure, audit-ready workflows, this book is an indispensable resource for anyone seeking to lead or innovate in the era of modern analytics engineering.
"dbt for Analytics Engineering" is a comprehensive guide for modern data practitioners seeking to master the evolving discipline of analytics engineering. The book begins by tracing the origins of analytics engineering and examining the emergence of the modern data stack, with an in-depth look at dbt's transformative role in shaping data workflows, architectural patterns, and large-scale organizational adoption. Through real-world case studies and expert insights, readers will gain a foundational understanding of how dbt enables efficient, collaborative, and scalable data transformation practices within diverse business contexts.
Diving into advanced project architecture, the book offers practical frameworks for structuring scalable dbt projects, managing configurations across multiple environments, and implementing robust model materializations. Readers will learn to harness Jinja and macros for code reusability, ensure high-performance data modeling using dimensional and Data Vault approaches, and adopt modular design patterns that optimize both maintainability and analytical clarity. In addition, dedicated chapters address the rigorous testing, quality assurance, and data governance practices needed to ensure trust, compliance, and discoverability in enterprise data assets.
The practical reach of "dbt for Analytics Engineering" extends to cloud warehouse optimization, orchestration, automation, and CI/CD integration, providing readers with strategies for deploying and managing analytics projects at enterprise scale. The book concludes by exploring the technological frontiers of analytics engineering-from integrating machine learning and real-time data streaming to building custom dbt plugins and embracing federated data models. With actionable guidance on scaling analytics teams, managing dependencies, and implementing secure, audit-ready workflows, this book is an indispensable resource for anyone seeking to lead or innovate in the era of modern analytics engineering.
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