Addressing the gap

Splunk Observability Cloud excelled at application and infrastructure monitoring but lacked the end-to-end insight that database monitoring would provide. That gap forced users to switch tools, even as public forum requests and AppDynamics’ $50M ARR success showed clear demand. Launching database monitoring would close the application-database performance divide and unlock new personas like database administrators, thereby expanding Splunk Observability Cloud’s total addressable market.

My role

I led design to bring Database monitoring to market, supporting a 12-person product and engineering team. Database monitoring also needed to integrate with application performance monitoring, and infrastructure monitoring, leading to close collaboration with their respective product and design teams.

Impact

Alpha launch of Database monitoring had 11 multinational enterprise customers with a conversion rate of 64% within 3 months of launch. This number of customers is currently estimated to grow to 25 by the end of Q1 2026.

“Our database team use SolarWinds. dbmon [sic database monitoring] gives me query plan information that I’ll only know once the application developer knows the issue is with the database… and when the database team will be looped in eventually to the ticket.”

Launching an MVP

The product roadmap prioritized the following features:

1. Surface vital metrics per database query,
2. Visualize query execution plans, and mark slow operations,
3. Correlate database queries with application traces, and
4. Launch database monitoring with AI-assisted workflows.

I also provided cross-functional leadership on initiatives including:

1. In-product marketing to encourage product-led adoption, and
2. Paving the way for future AI-assisted workflows. Database monitoring is the first vertical in Splunk Observability Cloud to ship with contextualized AI-assisted features.

Design overview

The following prototypes, information architecture diagrams, and journey maps provide a glimpse into the kind of artifacts I create to communicate design solutions.

Prototypes

This prototype takes the user from troubleshooting a trace to investigating the associated database query, and optimizing them using AI-assisted recommendations.

Other notable work

Following are other notable design contributions I made as part of the database monitoring initiative.

The query execution plan visualization

The query execution plan was visualized using a tree structure to represent the various operations involved in executing a database query. Micro-interactions were obsessed over to ensure that the tree structure was intuitive and easy to navigate. This is now part of the Splunk UI’s design system.

In the Figma embed below, you can see how I decompose components into atomic parts which are then assembled to make the final component.

Defining AI-assisted workflows

I defined and designed AI-assisted workflows such as query summarization, and AI-based recommendations to help users optimize their database queries. These features were prioritized based on competitive analysis, and engineering complexity. These are currently in customer preview and are getting positive feedback.

On demand AI-generated query summarization

Query summarization is a quick-win, highly deterministic AI-assisted solution to the problem of, “What is this SQL query trying to do?” A question that gets asked very often by SREs who are not the developers who wrote the query in the first place. This helps them figure out everything from the importance of said query, troubleshooting the query, to contacting the appropriate application team if all else fails.

AI recommendations for query optimization

AI recommendations was an obvious next step to utilize AI to help users optimize their slow-running database queries. The design took into consideration that there were engineering constraints which prevented ahead-of-time AI analysis of ingested queries. Hence, the design focused on an on-demand system where users could request AI recommendations for specific queries they were investigating.

In-product marketing to enable product-led adoption

This additional initiative targeted the opportunity to educate users about database monitoring within the product itself. Instead of relying on the stark zero state that Splunk Observability Cloud was using for existing verticals, I designed an in-product marketing experience that would educate users about the value proposition of database monitoring, and encourage them to contact the sales team to enable the feature.