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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

Data Modeling for Data Engineering

In this chapter, we will introduce what a data model is and why we need data modeling.

At the base of a relational database, there is the Entity-Relationship (E-R) model. Therefore, you will learn how you can use E-R models to represent data models that describe the data you have or want to collect.

We will present the E-R notation, cardinality, optionality, and the different levels of abstraction and of keys that you can have in a data model, and we will introduce two different notations commonly used in the industry, throughout the different examples that we will discuss.

We will explain a few special use cases of data models, such as weak entities or hierarchical relations, discussing their peculiarities or how they are usually implemented.

We will also introduce you to some common problems that you will face with your data, how to avoid them if possible, and how to recognize them if you cannot avoid them.

By the end of the chapter...

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