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Data Engineering with dbt
Data Engineering with dbt

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

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€37.99
Paperback Jun 2023 578 pages 1st Edition
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Profile Icon Zagni
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€37.99
Paperback Jun 2023 578 pages 1st Edition
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€20.98 €29.99
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€37.99
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Free Trial
Renews at €18.99p/m
eBook
€20.98 €29.99
Paperback
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Data Engineering with dbt

The Basics of SQL to Transform Data

This book is about engineering data platforms with dbt. When working with dbt, you write code in Structured Query Language (normally referred to as SQL, pronounced sequel or as its letters: es-qu-el), with a spritz of Jinja templating.

This means that when we are not discussing architectural and design topics, we will be reading and writing code in SQL, so you should get up to speed with SQL to work proficiently with dbt.

In this chapter, you will review, or learn from scratch, the basics of SQL, becoming familiar with this powerful language that you find everywhere when working with data.

First, we will introduce SQL and walk you through its core concepts, so that even if you are new to this language, you can start building your competence and follow the rest of this chapter and the book.

To complement this introduction, you will quickly be presented with all the main SQL commands, organized by category of use. This will ensure that you know the most relevant parts of SQL and you can use this chapter as a quick reference when you start writing your code.

You will then be guided through how to create an account in Snowflake, the leading cloud database that is very often used with dbt. You will use Snowflake in the cloud, without the need to install anything, and it is free for a trial period.

Once you have a Snowflake account, you will be guided through creating your first role, user, and database.

With your cloud database ready and the basic SQL commands presented, we will look deeper into the SELECT statement and the JOIN clause, as they will be crucial in working with dbt.

Analytical and window functions are presented at the end of the chapter in a simple way, suitable for beginners as well as mid- to senior-experienced SQL users; these advanced functionalities will power up your SQL skills, vastly widening your abilities.

To summarize, in this chapter, we are going to cover these topics:

  • Introducing SQL
  • SQL basics – core concepts and commands
  • Setting up a Snowflake database with users and roles
  • Querying data in SQL – syntax and operators
  • Combining data in SQL – the JOIN clause
  • Advanced – introducing window functions

Technical requirements

This chapter does not assume any prior SQL knowledge and presents information from the basics to intermediate level. If you know SQL well, you can skip this chapter or just browse the more advanced topics.

All code samples of this chapter are available on GitHub at https://github.com/PacktPublishing/Data-engineering-with-dbt/tree/main/Chapter_01.

To run the samples, later in this chapter, we will guide you through setting up a Snowflake account and roles and creating your first database.

Introducing SQL

SQL was created in the 70s and by the end of the 80s, had become the de facto standard to interact with Relational Databases (RDBs), and it now powers most of the data management industry in the world.

Given its huge footprint and powerful abstractions, SQL has become a standard that anyone working with database systems eventually becomes familiar with. The expressive power of SQL is well understood and its knowledge is so ubiquitous that it has been taken into use beyond RDBs, with Database Management Systems (DBMSs) of all sorts providing a SQL interface even on top of many non-RDB systems.

Some of the great advantages of SQL are as follows:

  • The core SQL functionality was standardized in the 80s and yet SQL is still very much alive and well, evolving and adding new powerful functionalities as data management evolves while maintaining compatibility with previous versions.

Every database has its SQL quirks, but the logic is the same and most SQL code will work on multiple databases with little or no change.

Learn it now and use it forever, and with (almost) every database.

  • At its core, it has a simple, rigorous, and powerful syntax that reads like English sentences, so even non-tech people can grasp the basic idea, while professionals can express exactly what they want in a precise and concise way.

Most people can probably get a sense of what the following SQL does:

SELECT ORDER_ID, CUSTOMER_CODE, TOTAL_AMOUNT
FROM ORDERS
WHERE YEAR(ORDER_DATE) = 2021;
  • With SQL, you work at the logical level, so you do not have to deal with implementation details, and it is a declarative language; you describe in a rigorous way what you want to achieve, not how to do it. The database engine has the freedom to store data, be implemented, and perform the request in its own way, as long as it produces the correct result according to SQL specifications.
  • With a single SQL statement, you can process one piece of data or billions, leaving the burden of finding the most effective way to the database and giving you some freedom from scale.
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Key benefits

  • 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

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.

Who is this book 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.

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
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Length: 578 pages
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Table of Contents

20 Chapters
Part 1: The Foundations of Data Engineering Chevron down icon Chevron up icon
Chapter 1: The Basics of SQL to Transform Data Chevron down icon Chevron up icon
Chapter 2: Setting Up Your dbt Cloud Development Environment Chevron down icon Chevron up icon
Chapter 3: Data Modeling for Data Engineering Chevron down icon Chevron up icon
Chapter 4: Analytics Engineering as the New Core of Data Engineering Chevron down icon Chevron up icon
Chapter 5: Transforming Data with dbt Chevron down icon Chevron up icon
Part 2: Agile Data Engineering with dbt Chevron down icon Chevron up icon
Chapter 6: Writing Maintainable Code Chevron down icon Chevron up icon
Chapter 7: Working with Dimensional Data Chevron down icon Chevron up icon
Chapter 8: Delivering Consistency in Your Data Chevron down icon Chevron up icon
Chapter 9: Delivering Reliability in Your Data Chevron down icon Chevron up icon
Chapter 10: Agile Development Chevron down icon Chevron up icon
Chapter 11: Team Collaboration Chevron down icon Chevron up icon
Part 3: Hands-On Best Practices for Simple, Future-Proof Data Platforms Chevron down icon Chevron up icon
Chapter 12: Deployment, Execution, and Documentation Automation Chevron down icon Chevron up icon
Chapter 13: Moving Beyond the Basics Chevron down icon Chevron up icon
Chapter 14: Enhancing Software Quality Chevron down icon Chevron up icon
Chapter 15: Patterns for Frequent Use Cases Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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