Analytics Engineering with SQL and dbt

Chapter 6. Building an End-to-End Analytics Engineering Use Case

Welcome to the last chapter of our book on analytics engineering with dbt and SQL. In the previous chapters, we have covered various concepts, techniques, and best practices for turning raw data into actionable insights using analytics engineering. Now it’s time to pull these topics all together and embark on a practical journey to construct an end-to-end analytics engineering use case.

In this chapter, we will look at designing, implementing, and deploying a comprehensive analytics solution from start to finish. We will leverage the full potential of dbt and SQL to build a robust and scalable analytics infrastructure and also use data modeling for both operational and analytical purposes.

Our main goal is to show how the principles and methods covered in this book can be practically applied to solve real-world data problems. By combining the knowledge acquired in the previous chapters, we will build an analytics engine that spans all phases of the data lifecycle, from data ingestion and transformation to modeling and reporting. Throughout the chapter, we’ll address common challenges that arise during implementation and provide guidance on how to effectively overcome them.

Problem Definition: An Omnichannel Analytics Case

In this challenge, our goal is to enhance the customer experience by providing seamless and personalized interactions across multiple channels. To achieve this, we need a comprehensive dataset ...