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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

Introducing SQL

SQL was created in the 70s and by the end of the 80s, had become the de facto standard to interact with Relational Databases (RDBs), and it now powers most of the data management industry in the world.

Given its huge footprint and powerful abstractions, SQL has become a standard that anyone working with database systems eventually becomes familiar with. The expressive power of SQL is well understood and its knowledge is so ubiquitous that it has been taken into use beyond RDBs, with Database Management Systems (DBMSs) of all sorts providing a SQL interface even on top of many non-RDB systems.

Some of the great advantages of SQL are as follows:

  • The core SQL functionality was standardized in the 80s and yet SQL is still very much alive and well, evolving and adding new powerful functionalities as data management evolves while maintaining compatibility with previous versions.

Every database has its SQL quirks, but the logic is the same and most SQL code will work on multiple databases with little or no change.

Learn it now and use it forever, and with (almost) every database.

  • At its core, it has a simple, rigorous, and powerful syntax that reads like English sentences, so even non-tech people can grasp the basic idea, while professionals can express exactly what they want in a precise and concise way.

Most people can probably get a sense of what the following SQL does:

SELECT ORDER_ID, CUSTOMER_CODE, TOTAL_AMOUNT
FROM ORDERS
WHERE YEAR(ORDER_DATE) = 2021;
  • With SQL, you work at the logical level, so you do not have to deal with implementation details, and it is a declarative language; you describe in a rigorous way what you want to achieve, not how to do it. The database engine has the freedom to store data, be implemented, and perform the request in its own way, as long as it produces the correct result according to SQL specifications.
  • With a single SQL statement, you can process one piece of data or billions, leaving the burden of finding the most effective way to the database and giving you some freedom from scale.
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