Databricks SQL Essentials - Array Data Type

🔖 This post is part of my series Databricks SQL Essentials Why Working with Array Types In this post, I want to focus on array types in Databricks SQL —what they are, why they matter, and how you can use them effectively. Arrays are powerful because they allow you to store multiple values in a single column , which can be incredibly useful when working with semi-structured data like JSON, logs, or event streams. We will look at two common scenarios: From Row to Array – combining multiple rows into a single array for easier aggregation. From Array to Row – exploding an array into separate rows to analyze individual elements. These techniques help you move smoothly between structured and semi-structured data for more flexible analysis. In this blog post, we will use the WanderBricks dataset in Databricks and show how to handle both cases: converting from rows to arrays and from arrays to rows . Array vs Set First, let’s start with a bit of theory about arrays. In this context, we will us