Undo/Redo for filters: Previously, only drill-downs and break-downs were reversible. Now, every step of your analysis โ from filtering to drilling down โ can be undone or redone, giving you more freedom to explore without worry.
History view: Right-click on Undo or Redo to see your complete exploration history at a glance.
We've improved the relationship list view to make it much easier to understand your data model connections at a glance.
When reviewing relationshipsโespecially in complex data modelsโit can be challenging to quickly distinguish between model names and field names. We've added visual indicators to help you parse this information more efficiently.
Field type icons โ Each field now displays an icon indicating its data type, making it easier to understand the relationship at a glance
Visual separation โ Model names and field names are now clearly differentiated with color coding, where model names appear in a lighter gray while field names stand out in bold
Better scannability โ The improved layout helps you quickly navigate through long lists of relationships without getting lost in the text
This enhancement is particularly helpful when you're working with unfamiliar data models or need to quickly verify relationship configurations across multiple tables. The visual cues reduce cognitive load and help you spot patterns or issues more quickly.
When a user clicks "drill down" or "break down," the default dimension list displays all available dimensions from the dataset. Holistics allows you to customize this dimension list to enhance your users' experience.
For example, you can highlight the most relevant dimensions and organize them into groups such as Locations, Products, and User Demographics as shown in the video below.
When viewing underlying data, the default table shown may not always be relevant to users' needs. Now, you can โโcustomize different views of the underlying dataโ to enhance your users' experience.
In this example, when examining the underlying data for the Revenue metric, users can select from different views, such as Orders, Users, or Products.
Before: New dashboards automatically inherited the base Holistics look, meaning users had to manually spend time aligning new dashboards with your structural and brand requirements.
Now: Admins can define one master template that is automatically applied to every new dashboard created.
This guarantees organizational consistency from the moment a user starts building their dashboard.
Step 1: Create a template: A dashboard template can include:
Visual styling (themes, colors, branding)
Structured layouts and pre-configured components
Defined block interactions, and much more.
Step 2: Set the default template: Once marked as the default, this template will be automatically applied to all new dashboards across the organization.
Keeping your workspace clean is important, but determining which content is actually unused is the challenging part. We are excited to introduce Archive Recommendations, a smart feature that automatically identifies inactive content so that you can archive with confidence.
Great news, the Merge Request workflow for GitLab has officially been released.
This is a new feature that brings our PR Workflow goodness to GitLab MRs so you can review, approve, and ship analytics changes using the process your team already knows.
What you will get:
โก One-click MR creation from Holistics, with AI to draft the MR title and description.
๐ Live MR status inside Holistics โ no more tab-hopping to GitLab.
๐ Auto-publish on merge: when an MR is approved and merged into your master branch, the changes publish themselves.
๐ Better visibility and control over changes, helping keep data reliable and audits painless.
If your team runs on GitLab, this will make reviews smoother, governance tighter, and shipping safer โ without changing how you work.
Have you ever received JSON data in your dataset and wished you could unnest it right there, without having to go back and modify your data model?
Previously, you'd need to:
๐ Navigate back to the model
๐ Create a SQL dimension
โ Write custom SQL just to extract fields
Why? Because AQL doesn't yet have built-in support for these database-specific functions (like JSON unnesting).
Now, with SQL Passthrough for AQL, you can do this directly in your dataset! ๐
No more back-and-forth โ just write AQL and leverage native SQL functions to handle JSON (and much more).
This isnโt limited to JSON unnesting โ any database-specific functionality not available in AQL can now be tapped into using passthrough.
๐ How it works:
SQL Passthrough functions act as a bridge, letting you call native SQL functions from your underlying database while maintaining type safety inside AQL queries.
Filter Visualization Directly on the Dashboard: You can now apply filters to any widget right from the dashboard. No need to pre-create filter controls or open the full data exploration. There are two ways you can do that: