Pivottable

A Pivot Table summarizes large datasets into a grid using grouping and aggregation. Use it to compare totals across multiple dimensions.

A Pivot Table is used to summarize, group, and analyze large datasets. It turns raw data into structured totals, counts, and comparisons across multiple dimensions.

What it represents

A Pivot Table organizes data into:

• Rows: grouping fields

• Columns: breakdown fields

• Values: metrics such as count, sum, or average

It converts many records into a compact analytical view.

What data it uses

The Pivot Table works with tabular data that contains:

• Dimensions: labels like service, pod, region, time

• Measures: numbers like count, duration, size, usage

Example input:

service  endpoint  requests
A        /login    120
A        /pay      80
B        /login    200

How the pivot works

You choose:

• Row fields → how data is grouped

• Column fields → how data is split

• Value field → what is calculated (count/sum/avg)

Example:

• Rows = service

• Columns = endpoint

• Values = count or sum(requests)

This answers:

“How many requests did each service send to each endpoint?”

What this visualization tells you

A Pivot Table helps you:

• Compare categories side by side

• See totals and subtotals

• Identify heavy usage or gaps

• Analyze patterns across dimensions

It’s ideal for multi-dimensional analysis.

When to use a Pivot Table

Use a Pivot Table when:

• You have large datasets

• You need grouping and aggregation

• You want to slice data by multiple fields

Typical use cases:

• Logs by service and endpoint

• Metrics by region and time

• Costs by team and project

• Alerts by severity and component

When not to use a Pivot Table

Avoid Pivot Tables when:

• You only need a single number

• You want trends over time

• You need a visual pattern

Use charts instead for those cases.

Pivot Tables work best when row and column cardinality are reasonable. High-cardinality fields can make the grid unreadable.