Scaling the BI Pixie Dashboard

As your organization generates more usage telemetry across Power BI reports, the BI Pixie Dashboard has to load and refresh a growing volume of data. How you scale depends on your deployment. Find yours below.

Note: refresh limits never cost you data. BI Pixie keeps collecting and storing every event regardless of how the dashboard is refreshed — scaling only changes how that data is loaded for analysis.

BI Pixie Cloud (SaaS) — fully managed

On BI Pixie Cloud there is nothing to configure. We operate the data pipeline and the dashboard for you and scale them automatically, so you don't run into refresh limits regardless of volume. The rest of this page applies only if you run BI Pixie yourself.

Have Microsoft Fabric? Use the Notebook + Direct Lake (most scalable)

If you have Microsoft Fabric — either through the BI Pixie Workload or a self-hosted deployment with a Fabric capacity — the most scalable architecture is the BI Pixie ETL Notebook with Direct Lake:

• A PySpark notebook ingests only new telemetry on each run (incremental) into a Lakehouse, using Spark compute that scales to years of data.
• The semantic model reads the Lakehouse via Direct Lake — there is no import step and no Power Query refresh, so refresh timeouts are eliminated entirely, regardless of data volume or retention period.

This is the default architecture for the BI Pixie Workload's Direct Lake dashboard, and it is the recommended target for self-hosted customers who have a Fabric capacity. Contact support to enable it for your environment.

Self-Hosted on a Power BI Pro license — options for refresh timeouts

When you run BI Pixie yourself, the dashboard's semantic model imports usage data through Power Query. On a workspace assigned to a Power BI Pro license, that refresh is capped at two hours — a Power BI platform limitation. If you hit timeouts due to large data volumes, work through these options (or, best of all, move to the Notebook + Direct Lake architecture above if you have Fabric):

1. Limit the data range. Change the Last N Days parameter from -1 to 30 to import only the last 30 days, then refresh again.
2. Scope the heatmap. If you activated the heatmap, turn it on only for the specific reports that need it.
3. Upgrade Power Platform → Azure. If you run BI Pixie on the Power Platform, move to the Azure deployment. On Azure the dashboard imports data far more efficiently through the Azure Storage Account; the Power Platform version's Dataverse connector is intended for low usage volumes.
4. Optimize performance parameters. On the Azure deployment with the Performance feature enabled, lower the Last N Days Log Analytics parameter (default 30) to reduce refresh time.
5. Use the PBIX semantic model. Professional and Enterprise customers with source-code access can refresh the provided PBIX file outside the Power BI Pro service limit.
6. Assign a Fabric capacity. Assigning the BI Pixie workspace to a Fabric capacity raises the refresh limit to 24 hours and lets you use incremental refresh for selected tables, or split the load across multiple dashboards filtered by project or workspace.
7. Move to the Notebook + Direct Lake architecture. The most scalable option — see the Fabric section above. Recommended once you have a Fabric capacity.
8. Professional services. If the options above don't meet your requirements (for example, processing several years of data in under 24 hours with Spark compute), open a support ticket and we can offer paid training or professional services to build your analytics solution on the data BI Pixie collects.

We recommend you open a support ticket and our team will guide you to the best resolution for your environment. The following table summarizes the options and support coverage: