Case Study

Query Builder

Query Builder is the Cloudaware CMDB section where users explore their infrastructure data. Cloudaware works with AWS, Azure and GCP, and one query can touch thousands of resources — spotting any pattern in that volume of data was the main challenge.

The problem

The Cloudaware query builder was overloaded and hard to understand.

The interface gave no guidance on the order of actions — users did not know where to start a query or in what order to set conditions, which led to configuration errors and abandoned queries.
No data aggregation or grouping — with thousands of resources, a raw table could not reveal patterns (for example, the distribution of resources by region, type or tag), forcing users to export the data and process it manually in Excel or third-party BI tools.

This created a cascade of problems: a growing support load, lost trust in the feature during onboarding, manual engineering effort on analysis the product should have handled itself, and ultimately low feature adoption, even though users already had the data for insights.

How I solved it

Better UX

A step-by-step query creation path that guides the user from the first filter to a saved query, with no need to know the data structure or the builder's logic in advance.

Fuller functionality

The data was connected through Google BigQuery, which made it possible to add aggregation, grouping and a charts section. Queries became far more meaningful: instead of a flat table, users get a result ready for analysis right inside the product, with no export to external tools.

Easier navigation

Switching between builder sections was improved: the key sections were moved to a single level and are available through tabs, with no nested menus or extra clicks.

The impact

Query Builder went from a tool for specialists to a clear step-by-step path. Aggregation and charts powered by BigQuery, together with simpler tab navigation, turned flat tables into a result users can work with right away, without leaving the product.

−55%

Faster query creation

Less time to build a typical query, from picking columns to seeing results.

~35%

More saved queries

More queries saved and shared across teams after the redesign.

−45%

Support requests

Fewer tickets about how to build or fix a query.

~2×

Feature adoption

More users trying charts and grouping within their first query.