Crafting a Visual Query Language
Transforming J1's Query Language Into a User-Friendly Tool
Transforming J1's Query Language Into a User-Friendly Tool
Transforming J1's Query Language Into a User-Friendly Tool
JupiterOne was stuck. Customers saw the platform's value but couldn't get past its proprietary query language. We needed to lower the barrier to entry.
JupiterOne was stuck. Customers saw the platform's value but couldn't get past its proprietary query language. We needed to lower the barrier to entry.
JupiterOne was stuck. Customers saw the platform's value but couldn't get past its proprietary query language. We needed to lower the barrier to entry.
Role
Principal Product Designer
Featured Skills
Interaction Design, UI Design, Prototyping
Impact
Reduced customer time to value with querying from 21 days to 1 day.
JupiterOne integrated enterprise cloud infrastructure into a centralized graph-based platform to unlock cybersecurity insights.
The key to unleashing the platform's value? JupiterOne's proprietary graph query language, J1QL.
A single query could power:
Data visualization
Automation
Security compliance.
They were the centerpiece of each workflow.
JupiterOne integrated enterprise cloud infrastructure into a centralized graph-based platform to unlock cybersecurity insights.
The key to unleashing the platform's value? JupiterOne's proprietary graph query language, J1QL.
A single query could power:
Data visualization
Automation
Security compliance.
They were the centerpiece of each workflow.
JupiterOne integrated enterprise cloud infrastructure into a centralized graph-based platform to unlock cybersecurity insights.
The key to unleashing the platform's value? JupiterOne's proprietary graph query language, J1QL.
A single query could power:
Data visualization
Automation
Security compliance.
They were the centerpiece of each workflow.
Queries at the core of the workflow.
Queries at the core of the workflow.
Queries at the core of the workflow.
While J1QL was approachable for a programming language, it was still programming. There was a lot of syntax and nuance that users needed to understand before they could leverage it fully.
This was a blocker for many prospective customers.
They needed an easier way to get value from the platform quickly.
While J1QL was approachable for a programming language, it was still programming. There was a lot of syntax and nuance that users needed to understand before they could leverage it fully.
This was a blocker for many prospective customers.
They needed an easier way to get value from the platform quickly.
While J1QL was approachable for a programming language, it was still programming. There was a lot of syntax and nuance that users needed to understand before they could leverage it fully.
This was a blocker for many prospective customers.
They needed an easier way to get value from the platform quickly.
Query features in code syntax.
Rather than distance users from the query language we leaned into its importance.
We mirrored query syntax in the visual interface.
We wanted to give users an accessible entry point to J1QL that would highlight its value while preparing them to write more complex queries.
Rather than distance users from the query language we leaned into its importance.
We mirrored query syntax in the visual interface.
We wanted to give users an accessible entry point to J1QL that would highlight its value while preparing them to write more complex queries.
Rather than distance users from the query language we leaned into its importance.
We mirrored query syntax in the visual interface.
We wanted to give users an accessible entry point to J1QL that would highlight its value while preparing them to write more complex queries.
Code & UI side-by-side.
The complexity of the data and interaction design required tight design and engineering collaboration.
Our team of two (designer + engineer) continuously cycled between drawings, Figma, and browser-based prototypes.
Dozens of iterations to dial in every detail.
The complexity of the data and interaction design required tight design and engineering collaboration.
Our team of two (designer + engineer) continuously cycled between drawings, Figma, and browser-based prototypes.
Dozens of iterations to dial in every detail.
The complexity of the data and interaction design required tight design and engineering collaboration.
Our team of two (designer + engineer) continuously cycled between drawings, Figma, and browser-based prototypes.
Dozens of iterations to dial in every detail.
Add-remove a data class.
Add a property filter.
Add a property filter to your data class.
Toggle direction of graph relationship.
On top of learning the syntax, users faced another major hurdle: learning the data model. What could they query for?
JupiterOne offered over 100+ data classes and 1000+ data types. An overwhelming number for a newcomer!
With the visual query builder, we enabled users to easily browse and search the data model in context.
On top of learning the syntax, users faced another major hurdle: learning the data model. What could they query for?
JupiterOne offered over 100+ data classes and 1000+ data types. An overwhelming number for a newcomer!
With the visual query builder, we enabled users to easily browse and search the data model in context.
On top of learning the syntax, users faced another major hurdle: learning the data model. What could they query for?
JupiterOne offered over 100+ data classes and 1000+ data types. An overwhelming number for a newcomer!
With the visual query builder, we enabled users to easily browse and search the data model in context.
Data model explorer sections.
Query results could be returned/visualized as either a datatable or graph.
Query results could be returned/visualized as either a datatable or graph.
Query results could be returned/visualized as either a datatable or graph.
Toggle between graph and table data views.
Styles, structure, and functionality continued to evolve from internal release to beta to general availability.
Here's a short video from the beta to give you the gist of that moment in time.
Styles, structure, and functionality continued to evolve from internal release to beta to general availability.
Here's a short video from the beta to give you the gist of that moment in time.
Styles, structure, and functionality continued to evolve from internal release to beta to general availability.
Here's a short video from the beta to give you the gist of that moment in time.
A short video from the Beta release for reference of that point in time.
Before, it took customers an average of 21 days to write and save a custom J1QL query after integrating their data. With the query builder, that dropped to 1 day.
Our customers and professional services team loved it!
To follow up on the success, we prioritized building advanced query functionality we had descoped from the first release.
Before, it took customers an average of 21 days to write and save a custom J1QL query after integrating their data. With the query builder, that dropped to 1 day.
Our customers and professional services team loved it!
To follow up on the success, we prioritized building advanced query functionality we had descoped from the first release.
Before, it took customers an average of 21 days to write and save a custom J1QL query after integrating their data. With the query builder, that dropped to 1 day.
Our customers and professional services team loved it!
To follow up on the success, we prioritized building advanced query functionality we had descoped from the first release.
Customer feedback via our Chief Revenue Officer.
Customer feedback via our Chief Revenue Officer.
Customer feedback via our Chief Revenue Officer.
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©2024 Patrick Morgan
A globally respected source for creative technology insights. 5200+ tech pros in all 50 US states and 121 countries read each week.
Open to Work
©2024 Patrick Morgan
A globally respected source for creative technology insights. 5200+ tech pros in all 50 US states and 121 countries read each week.
Open to Work
©2024 Patrick Morgan