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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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$27.98 $39.99
Book Jun 2023 578 pages 1st Edition
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$27.98 $39.99
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$49.99
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Renews at $19.99p/m
Arrow left icon
Profile Icon Zagni
Arrow right icon
$27.98 $39.99
Book Jun 2023 578 pages 1st Edition
eBook
$27.98 $39.99
Print
$49.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$27.98 $39.99
Print
$49.99
Subscription
Free Trial
Renews at $19.99p/m

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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.

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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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 30, 2023
Length 578 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781803246284
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What do you get with eBook?

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Product Details

Publication date : Jun 30, 2023
Length 578 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781803246284
Category :
Languages :
Concepts :
Tools :

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Table of Contents

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