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Building a Pragmatic Data Platform with dbt and Snowflake
Paperback
Publisher: Technics Publications
Published: Jun 2 2026
Pages: 398
Weight: 2.02
Height: 0.82 Width: 8.50 Depth: 11.00
Language: English
Transform scattered data into a scalable, governed, and business-ready modern data platform using proven dbt and Snowflake patterns that simplify architecture, accelerate delivery, and keep your team focused on solving real problems instead of wrestling with unnecessary complexity. Building a Pragmatic Data Platform with dbt and Snowflake provides a hands-on roadmap for creating modern cloud data platforms that are practical, maintainable, and built for the real world. Data architects, analytics engineers, data engineers, BI leaders, and technical managers will discover how to design a data platform that balances governance with agility while supporting analytics, AI, reporting, APIs, and enterprise-scale workloads.
Rather than drowning readers in theory, Roberto Zagni and Jakob Brandel present battle-tested strategies for building data platforms that actually work in production environments. To accelerate your implementation, the authors provide two enterprise-proven dbt packages: the Pragmatic Data Platform package and the Snowflake Project Admin package. The book explains these packages in detail, using the realistic Stonks sample project as a hands-on playbook to show you exactly how to deploy them step-by-step. Apply modern DataOps practices. Design layered data architectures. Build automated ingestion pipelines. Engineer reliable storage, refined, and delivery layers. Develop scalable dbt projects with reusable macros, CI/CD workflows, automated testing, version management, historization, and modular domain-driven design.
Readers will explore practical approaches to data modeling, data governance, data mesh, security, PII handling, release management, and cloud-native analytics engineering using Snowflake and dbt Cloud. Every chapter focuses on practical implementation patterns, automation techniques, and scalable engineering workflows that reduce technical debt and improve collaboration across data teams. The book also compares architectural styles, including Kimball, Data Vault, Medallion Architecture, and Inmon approaches, so teams can confidently choose the right strategy for their organization.
Analyze real customer case studies from organizations modernizing their analytics environments with dbt and Snowflake. Optimize ingestion workflows. Build historical and versioned models. Create data marts, star schemas, and business-ready delivery layers that support reporting, machine learning, APIs, and self-service analytics.
Strengthen your ability to lead modern analytics initiatives with clear guidance grounded in years of enterprise experience. Evaluate tradeoffs between flexibility and governance. Integrate DevOps principles into analytics engineering. Simplify complex transformations with reusable dbt macros and testing frameworks. Build platforms that remain auditable, extensible, and resilient as business requirements evolve.
Whether you are migrating from legacy ETL systems, launching a new cloud data warehouse, modernizing business intelligence workflows, or building a future-ready data engineering practice, this book provides the architecture patterns, implementation guidance, and operational discipline needed to succeed with modern data platforms. Perfect for readers seeking books on dbt, Snowflake, analytics engineering, data architecture, DataOps, cloud data platforms, data warehousing, a modern data stack, ELT pipelines, data modeling, data governance, scalable analytics, business intelligence, data mesh, dimensional modeling, and enterprise data engineering.
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