Group Bucket

When analyzing data, we often want to understand how certain values fall into meaningful ranges or categories.

For example, in our online store, what if we want to know how many products are low, medium, or high in stock? Or how many customers belong to each loyalty tier? This kind of grouped insight helps us spot patterns, allocate resources, and make decisions more effectively.

In this lesson, we’ll learn how to group data into buckets, such as predefined value ranges or categories, and then summarize each bucket using aggregate functions like COUNT, SUM, or AVG.

By the end of this lesson, we’ll be able to:

• Group continuous values into defined ranges (like price or stock levels).

• Use CASE WHEN, IF, or built-in functions to define bucket logic.

• Aggregate and summarize results per bucket.

Pattern overview

Category:

• Aggregation patterns

Intent:

Group continuous or categorical values into logical segments (buckets), and summarize each segment using aggregates.

Motivation:

Raw numbers alone aren’t always meaningful. When we bucket data, we convert raw values into human-friendly segments. This helps answer questions like:

• How many products are low in stock?

• How many customers are VIPs vs. regular?

• What’s the average order value per spending range?

Also known as:

• Binning

• Range aggregation

• Grouping by range

• Histogram buckets

Structure

This pattern uses CASE, IF, or similar expressions to define custom labels or numeric ranges, and then groups by them. Aggregation functions are then used per group.