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Data Engineering with dbt

Data Engineering with dbt

By : Roberto Zagni
4.6 (9)
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Data Engineering with dbt

Data Engineering with dbt

4.6 (9)
By: Roberto Zagni

Overview of this book

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.
Table of Contents (21 chapters)
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1
Part 1: The Foundations of Data Engineering
7
Part 2: Agile Data Engineering with dbt
14
Part 3: Hands-On Best Practices for Simple, Future-Proof Data Platforms

Summary

Congratulations, you made it to the end of this book!

In this chapter, we presented some more advanced patterns for ingesting data into Snowflake and multiple extensions of the save_history macro for dealing with different use cases, from detecting deleted rows to ingesting multiple versions at once to managing PII in our history tables.

As the author, first and foremost, I hope that I have passed on some of my passion for software engineering and the pleasure to put together a well-architected, simple-to-understand project that makes it possible to onboard new colleagues and maintain and evolve the functionalities without losing any sleep.

One of the key goals of the team working on this book was to discuss data engineering and data architecture while presenting how dbt works and how it can be used to solve the common problems that we all face when building a data platform.

After decades of dealing with data, I have my opinionated way of doing things, and this...

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Data Engineering with dbt
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