Databricks IntegrationConnect your Databricks lakehouse to KPI Tree and build the accountability layer your data is missing.
KPI Tree connects directly to your Databricks SQL Warehouse. Define metrics with SQL against your lakehouse tables, map how they drive each other, assign ownership to real people, and prove what worked. One query per metric on a configurable schedule, all comparisons and aggregations off-warehouse. Your Unity Catalog governance stays exactly as configured.
Connected in under an hour
Provide your SQL Warehouse connection details and a personal access token. KPI Tree validates the connection in real time.
Create a personal access token
In your Databricks workspace, generate a personal access token with read access to the schemas and tables you want to track. No admin privileges, no write access needed.
Provide your SQL Warehouse details
Enter your workspace host URL, the HTTP path to your SQL Warehouse, your token, and the target schema. KPI Tree validates the connection in real time, confirming it can authenticate and reach your tables.
Define metrics and start building
Write SQL directly against your lakehouse tables and views. One query per metric on a configurable schedule, all downstream analytics off-warehouse. Map how metrics drive each other, assign RACI ownership, and start closing the loop between data and action.
Built for the Databricks lakehouse
KPI Tree connects to your SQL Warehouse, respects your Unity Catalog governance, and runs all analytics off-warehouse to keep compute costs minimal.
Unity Catalog governance
KPI Tree connects as a read-only user through your SQL Warehouse. Your existing Unity Catalog permissions, column masks, and row filters all apply. No parallel permission system to maintain.
Off-warehouse analytics
One query per metric on a configurable schedule. Comparisons, correlations, regressions, and outlier detection all run in KPI Tree's compute engine. Your SQL Warehouse auto-suspends between syncs, so you pay only for the seconds of compute each sync cycle uses.
SQL Warehouse connection
Connect via your workspace host URL and SQL Warehouse HTTP path. Personal access token authentication. KPI Tree validates the connection in real time and provides deep links back to your workspace for auditability.
Cause-and-effect trees built from your lakehouse data.
Your lakehouse stores everything. KPI Tree reveals how it connects. Metric trees map how every KPI drives the ones above it - from operational inputs through to revenue outcomes. When a number moves, trace the tree to understand why. Assign RACI ownership so every metric has a named person accountable for it. Statistical correlation analysis surfaces relationships you might not have spotted. Root cause detection traces anomalies through the full tree automatically.
- Metric trees map causal relationships between business measures
- RACI ownership assigns accountability to real people
- Statistical correlation analysis surfaces hidden drivers
- Root cause detection traces anomalies through the tree
One query per metric. No additional queries for comparisons or aggregations.
KPI Tree runs one query per metric to sync data from your Databricks SQL Warehouse. Comparison periods, correlations, regressions, outlier detection, and causal analysis all run in KPI Tree's compute engine without pushing additional queries back to your warehouse. Your SQL Warehouse auto-suspends between sync cycles, keeping compute costs minimal.
- No additional warehouse queries for comparisons or aggregations
- SQL Warehouse auto-suspends between sync cycles
- Configurable sync schedule per metric
- All downstream analytics run off-warehouse
Your Unity Catalog governance stays intact.
KPI Tree connects through your SQL Warehouse as a read-only user. Unity Catalog permissions, column masks, row filters, and ACLs all apply to every query KPI Tree runs. No data is copied outside your governance boundary. No parallel permission system to maintain. Your lakehouse security posture stays exactly as your team configured it.
- Unity Catalog permissions enforced on every query
- Column masks and row filters respected
- Read-only access only
- All credentials encrypted at rest
Personalised action plans and impact tracking.
Dashboards show you what happened. KPI Tree closes the loop. Personalised action plans tell each team member what to focus on based on the metrics they own. When someone takes action, impact tracking verifies whether the metric moved. Notifications via Slack, Email, WhatsApp, or SMS keep owners informed. The result is a system that connects your lakehouse data to real behaviour change across your organisation.
- Personalised action plans delivered to each team member
- Impact tracking verifies whether actions moved the metric
- Notifications via Slack, Email, WhatsApp, and SMS
- Engagement heatmaps show who is acting on data
What KPI Tree adds on top of Databricks
Your lakehouse stores the data. KPI Tree adds the layer that turns it into understanding, ownership, action, and proof.
Cause and effect, not just queries
Metric trees map how every KPI drives the ones above it. When revenue drops, trace the tree to find whether it was traffic, conversion, or average order value - and who owns each driver.
Ownership that creates accountability
Every metric has a named owner with full RACI. Actions are tracked against the metric they were meant to move. Personalised action plans tell each team member what to focus on.
Off-warehouse analytics that keep costs flat
One query per metric syncs data into KPI Tree's engine. Comparisons, correlations, and outlier detection all run off-warehouse. Your Databricks costs stay predictable as your team grows.
Related integrations
Other data sources that work with KPI Tree.
Common questions
- Your Databricks workspace host URL, the HTTP path to your SQL Warehouse, a personal access token, and the target schema.
- Personal access tokens. Generate one in your Databricks workspace with read access to the schemas and tables you want to track.
- Yes. KPI Tree connects through your SQL Warehouse. All Unity Catalog permissions, column masks, row filters, and ACLs apply to every query.
- One query per metric on a configurable schedule. All comparisons, correlations, and analysis run off-warehouse. Your SQL Warehouse auto-suspends between sync cycles.
- Yes. If you use dbt, see our dedicated dbt Cloud and dbt Core integration pages. They sync your semantic layer and compile metrics into Databricks-dialect SQL.
- KPI Tree runs one lightweight query per metric on a configurable schedule. A small SQL Warehouse handles hundreds of metrics comfortably. The warehouse auto-suspends between syncs.
- KPI Tree queries your SQL Warehouse and processes aggregated results in its own engine. Raw row-level data is not persisted outside your environment. All Unity Catalog governance remains enforced.
- Yes. KPI Tree connects to any SQL Warehouse endpoint, whether provisioned, serverless, or classic.
Related guides
Deep dives into the frameworks and metrics that work with Databricks.
Connect Databricks in under an hour.
Workspace host, SQL Warehouse path, personal access token. Your lakehouse data with ownership, causal trees, and action plans on top.
