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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

Defining analytics engineering

We have seen in the previous section that with the advent of the modern data stack, data movement has become easier, and the focus has therefore switched over to managing raw data and transforming it into the refined data used in reports by business users. There are still plenty of cases where ad hoc integrations and ETL pipelines are needed, but this is not the main focus of the data team as it was in the past.

The other Copernican revolution is that the new data stack enables data professionals to work as a team, instead of perpetuating the work in isolation, which is common in the legacy data stack. The focus is now on applying software engineering best practices to make data transformation development as reliable as building software. You might have heard about DevOps and DataOps.

With this switch of focus, the term analytics engineering has emerged to identify the central part of the data life cycle going from the access to the raw data up...

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