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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

In this chapter, we learned about dimensional data and how it enhances the usability of data platforms and data marts. We learned how to incorporate dimensional data in our data platform, using the basic features of dbt.

We reinforced our understanding of the layers of our Pragmatic Data Platform that we learned about in the previous chapters and applied those principles to work with dimensional data.

In the next chapter, we will start exploring more advanced features that will allow us to replace some of the basic code to produce a more resilient and easier-to-write and easier-to-maintain data platform.

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Tech Concepts
36
Programming languages
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Data Engineering with dbt
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