Zagni Roberto (23 results)

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Paperback or Softback. Condition: New. Data Engineering with dbt: A practical guide to building a cloud-based, pragmatic, and dependable data platform with SQL. Book.

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Paperback. Condition: new. Paperback. 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. Transform scattered data into a scalable, governed, and business-ready modern data platform using dbt and Snowflake patterns that simplify architecture, accelerate delivery, and keep your team focused on solving problems. Th Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condition: new. Paperback. 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. Transform scattered data into a scalable, governed, and business-ready modern data platform using dbt and Snowflake patterns that simplify architecture, accelerate delivery, and keep your team focused on solving problems. </s Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condition: new. Paperback. 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. Transform scattered data into a scalable, governed, and business-ready modern data platform using dbt and Snowflake patterns that simplify architecture, accelerate delivery, and keep your team focused on solving problems. </s Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Taschenbuch. Condition: Neu. Building a Pragmatic Data Platform with dbt and Snowflake | Roberto Zagni (u. a.) | Taschenbuch | Englisch | 2026 | Technics Publications | EAN 9798898160494 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condition: Neu. Data Engineering with dbt | A practical guide to building a cloud-based, pragmatic, and dependable data platform with SQL | Roberto Zagni | Taschenbuch | Englisch | 2023 | Packt Publishing | EAN 9781803246284 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Use easy-to-apply patterns in SQL and Python to adopt modern analytics engineering to build agile platforms with dbt that are well-tested and simple to extend and runPurchase of the print or Kindle book includes a free PDF Elektronisches BuchKey Features Build a solid dbt base and learn data modeling and the modern data stack to become an analytics engineer Build automated and reliable pipelines to deploy, test, run, and monitor ELTs with dbt Cloud Guided dbt + Snowflake project to build a pattern-based architecture that delivers reliable datasetsBook Descriptiondbt Cloud helps professional analytics engineers automate the application of powerful and proven patterns to transform data from ingestion to delivery, enabling real DataOps.This book begins by introducing you to dbt and its role in the data stack, along with how it uses simple SQL to build your data platform, helping you and your team work better together. You'll find out how to leverage data modeling, data quality, master data management, and more to build a simple-to-understand and future-proof solution. As you advance, you'll explore the modern data stack, understand how data-related careers are changing, and see how dbt enables this transition into the emerging role of an analytics engineer. The chapters help you build a sample project using the free version of dbt Cloud, Snowflake, and GitHub to create a professional DevOps setup with continuous integration, automated deployment, ELT run, scheduling, and monitoring, solving practical cases you encounter in your daily work.By the end of this dbt book, you'll be able to build an end-to-end pragmatic data platform by ingesting data exported from your source systems, coding the needed transformations, including master data and the desired business rules, and building well-formed dimensional models or wide tables that'll enable you to build reports with the BI tool of your choice.What you will learn Create a dbt Cloud account and understand the ELT workflow Combine Snowflake and dbt for building modern data engineering pipelines Use SQL to transform raw data into usable data, and test its accuracy Write dbt macros and use Jinja to apply software engineering principles Test data and transformations to ensure reliability and data quality Build a lightweight pragmatic data platform using proven patterns Write easy-to-maintain idempotent code using dbt materializationWho this book is forThis book is for data engineers, analytics engineers, BI professionals, and data analysts who want to learn how to build simple, futureproof, and maintainable data platforms in an agile way. Project managers, data team managers, and decision makers looking to understand the importance of building a data platform and foster a culture of high-performing data teams will also find this book useful. Basic knowledge of SQL and data modeling will help you get the most out of the many layers of this book. The book also includes primers on many data-related subjects to help juniors get started.Table of Contents Basics of SQL to transform data Setting up your dbt Cloud development environment Data modelling for data engineering Analytics Engineering as the New Core of Data Engineering Transforming data with dbt Writing Maintainable Code Working with Dimensional Data Delivering Consistency In Your Code Delivering Reliability In Your Data Agile development Collaboration Deployment, Execution and Documentation Automation Moving beyond basics Enhancing Software Quality Patterns for frequent use cases.