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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

Adding dimensional data

In general, dimensional data is used to provide descriptive information about a fact by using the code of the dimension entity that is stored in the facts to join on the dimension table to retrieve the descriptive information.

The position fact that we loaded in the previous section has four explicit foreign keys, which we have aptly named with the _CODE suffix: the account code, the security code, the exchange code, and the currency code.

These four codes are the references, or foreign keys, to the four dimensions that we can directly connect to this fact.

There is also one extra implicit dimension, the bank dimension, which is implied in the names of the models.

Creating clear data models for the refined and data mart layers

To be able to finalize the dimensions and the fact design, we need to have a clear picture of the data model that we want to use in our reports (the data mart layer), which is often a star schema or, rarely, a wide table...

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