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Data Engineering with dbt

Data Engineering with dbt

By : Zagni
4.6 (9)
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Data Engineering with dbt

Data Engineering with dbt

4.6 (9)
By: 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

Enhancing Software Quality

In this chapter, you will discover and apply more advanced patterns that provide high-quality results in real-life projects, and you will experiment with how to evolve your code with confidence through refactoring.

Through the selection of small use cases around our sample project, you will learn how to save the history of changes of your entities in a very efficient way, how to detect deleted rows from a source, and how to use window functions to leverage the data stored in HIST tables to analyze data evolution over time.

In the last section of this chapter, you will create and apply a macro to properly handle the orphans keys in your facts using self-completing dimensions to produce better quality facts and dimensions for your data marts.

In this chapter, you will learn about the following topics:

  • Refactoring and evolving models
  • Implementing real-world code and business rules
  • Publishing dependable datasets
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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Data Engineering with dbt
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