Data Engineering with dbt: A practical guide to building a cloud-based, pragmatic, and dependable data platform with SQL

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.

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