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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

Analytics Engineering as the New Core of Data Engineering

In this chapter, we are going to understand the full life cycle of data, from its creation during operations to its consumption by business users. Along this journey, we will analyze the most important details of each phase in the data transformation process from raw data to information.

This will help us understand why analytics engineering, the part of the data journey that we focus on when working with dbt, besides remaining the most creative and interesting part of the full data life cycle, has become a crucial part of data engineering projects.

Analytics engineering transforms raw data from disparate company data sources into information ready for use with tools that analysts and businesspeople use to derive insights and support data-driven decisions.

We will then discuss the modern data stack and the way data teams can work better, defining the modern analytics engineering discipline and the roles in a data team...

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