Book Synopsis:

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 run. Purchase of the print or Kindle book includes a free PDF eBook.

Key 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 datasets

Book Description:

dbt 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 materialization

Who this book is for:

This 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:

1. Basics of SQL to transform data
2. Setting up your dbt Cloud development environment
3. Data modelling for data engineering
4. Analytics Engineering as the New Core of Data Engineering
5. Transforming data with dbt
6. Writing Maintainable Code
7. Working with Dimensional Data
8. Delivering Consistency In Your Code
9. Delivering Reliability In Your Data
10. Agile development
11. Collaboration
12. Deployment, Execution and Documentation Automation
13. Moving beyond basics
14. Enhancing Software Quality
15. Patterns for frequent use cases

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