DBT for Analytics Engineering on BigQuery (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"DBT for Analytics Engineering on BigQuery" Unlock the full potential of your data warehouse by mastering the intersection of analytics engineering, DBT, and Google BigQuery. "DBT for Analytics Engineering on BigQuery" offers a comprehensive, technical deep dive into building robust, scalable, and cost-efficient analytics pipelines in the cloud. The book begins by laying a strong foundation in BigQuery architecture and analytics engineering paradigms, exploring key concepts such as storage and compute separation, distributed query execution, data governance, and the transformational role of DB...
"DBT for Analytics Engineering on BigQuery"
Unlock the full potential of your data warehouse by mastering the intersection of analytics engineering, DBT, and Google BigQuery. "DBT for Analytics Engineering on BigQuery" offers a comprehensive, technical deep dive into building robust, scalable, and cost-efficient analytics pipelines in the cloud. The book begins by laying a strong foundation in BigQuery architecture and analytics engineering paradigms, exploring key concepts such as storage and compute separation, distributed query execution, data governance, and the transformational role of DBT within the modern data lifecycle.
Progressing beyond fundamentals, the text delivers practical guidance on advanced DBT features tailored for BigQuery, including incremental models, materializations, macros, and templating for reusable and flexible model design. Readers will learn sophisticated data modeling techniques-ranging from star and snowflake schemas to Data Vault and temporal modeling-while gaining hands-on strategies for ingesting semi-structured data, implementing change data capture, enforcing granular security, and automating documentation and data lineage. Integrated workflows for testing, validation, documentation, and data quality assurance ensure that every transformation meets enterprise-grade standards for reliability and compliance.
Culminating with real-world solutions for orchestration, DevOps, and emerging analytics applications, the book details best practices for scheduling, deployment, and monitoring of DBT projects in production, including CI/CD integration, cost optimization, and performance engineering. The final chapters look to the future, covering the rapid evolution of semantic layers, AI-driven observability, data contracts, and mesh architectures. Whether you are a data engineer, analytics professional, or technical leader, this guide equips you to harness DBT and BigQuery for next-generation analytics engineering at scale.
Unlock the full potential of your data warehouse by mastering the intersection of analytics engineering, DBT, and Google BigQuery. "DBT for Analytics Engineering on BigQuery" offers a comprehensive, technical deep dive into building robust, scalable, and cost-efficient analytics pipelines in the cloud. The book begins by laying a strong foundation in BigQuery architecture and analytics engineering paradigms, exploring key concepts such as storage and compute separation, distributed query execution, data governance, and the transformational role of DBT within the modern data lifecycle.
Progressing beyond fundamentals, the text delivers practical guidance on advanced DBT features tailored for BigQuery, including incremental models, materializations, macros, and templating for reusable and flexible model design. Readers will learn sophisticated data modeling techniques-ranging from star and snowflake schemas to Data Vault and temporal modeling-while gaining hands-on strategies for ingesting semi-structured data, implementing change data capture, enforcing granular security, and automating documentation and data lineage. Integrated workflows for testing, validation, documentation, and data quality assurance ensure that every transformation meets enterprise-grade standards for reliability and compliance.
Culminating with real-world solutions for orchestration, DevOps, and emerging analytics applications, the book details best practices for scheduling, deployment, and monitoring of DBT projects in production, including CI/CD integration, cost optimization, and performance engineering. The final chapters look to the future, covering the rapid evolution of semantic layers, AI-driven observability, data contracts, and mesh architectures. Whether you are a data engineer, analytics professional, or technical leader, this guide equips you to harness DBT and BigQuery for next-generation analytics engineering at scale.
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