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Fundamentals of Analytics Engineering

Fundamentals of Analytics Engineering

By : Dumky De Wilde, Kassapian, Gligorevic, Juan Manuel Perafan, Lasse Benninga, Ricardo Angel Granados Lopez, Taís Laurindo Pereira
4.7 (3)
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Fundamentals of Analytics Engineering

Fundamentals of Analytics Engineering

4.7 (3)
By: Dumky De Wilde, Kassapian, Gligorevic, Juan Manuel Perafan, Lasse Benninga, Ricardo Angel Granados Lopez, Taís Laurindo Pereira

Overview of this book

Written by a team of 7 industry experts, Fundamentals of Analytics Engineering will introduce you to everything from foundational concepts to advanced skills to get started as an analytics engineer. After conquering data ingestion and techniques for data quality and scalability, you’ll learn about techniques such as data cleaning transformation, data modeling, SQL query optimization and reuse, and serving data across different platforms. Armed with this knowledge, you will implement a simple data platform from ingestion to visualization, using tools like Airbyte Cloud, Google BigQuery, dbt, and Tableau. You’ll also get to grips with strategies for data integrity with a focus on data quality and observability, along with collaborative coding practices like version control with Git. You’ll learn about advanced principles like CI/CD, automating workflows, gathering, scoping, and documenting business requirements, as well as data governance. By the end of this book, you’ll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.
Table of Contents (23 chapters)
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1
Prologue
Free Chapter
2
Part 1:Introduction to Analytics Engineering
5
Part 2: Building Data Pipelines
11
Part 3: Hands-On Guide to Building a Data Platform
13
Part 4: DataOps
17
Part 5: Data Strategy
21
Index

Summary

In this chapter, we saw how integrating and deploying your code can be automated. We also discussed how analytics engineering is leveraging existing practices from software engineering when it comes to using CI/CD for automated tests and deployment to ensure code quality and delivery promptly. Testing code for formatting, compilation errors, and unit testing functions and macros is an essential part of testing your code before deploying to production.

In this chapter, we introduced the paradigm of DataOps, its ideas and principles, and how it is strongly influenced by practices from software engineering. Then, we introduced the concept of CI, discussed the necessity of testing your code for formatting issues, compilation and runtime errors, and covered unit testing your functions and macros. Next, we talked about CD, the concept of state comparison and idempotency, and enabling Slim CI/CD runs in dbt Cloud using state deferral to reduce costs and time. Lastly, we briefly...

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