Databricks
Databricks dashboard builder:
lakehouse to live apps.
Connect camelAI to your Databricks lakehouse and turn notebooks, Delta tables, and ML models into live applications your whole team can use. No separate BI stack. No infrastructure.
Your notebooks deserve an audience.
The gap between a data scientist's notebook and a stakeholder-ready app is enormous. camelAI bridges it in one conversation.
df = spark.sql("""
SELECT date,
SUM(revenue) AS total
FROM catalog.sales.daily
GROUP BY date
""")Total Revenue
$1.47M
+12.3%
Avg Daily
$52.1K
+8.7%
Best Day
$78.4K
Mar 8
Daily Revenue (Last 14 Days)
Every layer of your lakehouse. One connection.
camelAI connects to your Databricks SQL warehouse and has full access to your Delta Lake medallion architecture.
Bronze
Raw ingested data
Silver
Cleaned & enriched
Gold
Business-ready aggregates
Delta Lake
Lakehouse Storage
Bronze, silver, and gold tables. Petabytes of structured and unstructured data in your lakehouse.
Databricks
Unified Analytics
Spark SQL, Unity Catalog, MLflow, and notebooks. Your compute and governance layer.
camelAI
AI Agent
Connects to Databricks via SQL warehouse or cluster. Writes Spark SQL, builds apps, deploys instantly.
Live Apps
Published at *.camelai.app
Dashboards, model monitors, data catalogs — live at a URL your whole team can use.
Models in production need dashboards too.
Build ML observability dashboards from your MLflow data. Track accuracy, drift, and feature importance — share with stakeholders via a live URL.
churn_predictor_v3
XGBoost -- Production
Prediction Drift
Feature Drift
Data Quality
Latency p99
Model Accuracy (12 months)
Feature Importance
Spark SQL. Natively.
-- Unity Catalog + Delta Lake + window functions
WITH daily_metrics AS (
SELECT
date,
model_version,
AVG(prediction_accuracy) AS avg_accuracy,
COUNT(*) AS predictions,
LAG(AVG(prediction_accuracy), 1) OVER (
PARTITION BY model_version
ORDER BY date
) AS prev_accuracy
FROM ml_catalog.production.predictions
WHERE date >= '2026-01-01'
GROUP BY date, model_version
)
SELECT *,
ROUND(avg_accuracy - prev_accuracy, 4) AS accuracy_delta
FROM daily_metrics
WHERE avg_accuracy < prev_accuracy
ORDER BY accuracy_delta;camelAI writes Spark SQL natively — window functions, CTEs, Delta operations, Unity Catalog three-part names, and UDFs. If your SQL warehouse can run it, camelAI can write it.
Weeks of dashboarding, or one conversation.
Traditional BI on Databricks
BI tool license
Tableau or Power BI — $70–150/user/month
Data modeling layer
dbt project setup — 2–6 weeks of engineering
Dashboard development
Weeks of iteration with the BI team
ML model visibility
Custom monitoring — separate project entirely
Total time to first dashboard
4–12 weeks
+ ongoing BI tool costs
camelAI
One Databricks connection
SQL warehouse endpoint + token — 2 minutes
One conversation
Describe what you need — dashboards, monitors, catalogs
Published in minutes
Live app at a shareable URL with auto-refresh
ML + analytics — same tool
Model monitoring and business dashboards in one place
Total time to first dashboard
Minutes
Pay for what you use
Enterprise-ready. Lakehouse-native.
camelAI works within your existing Databricks governance and security model. No new attack surface.
camelAI respects your Unity Catalog permissions. Row-level security, column masking, and data lineage — all honored automatically.
Connect via Azure Private Link or AWS PrivateLink. Your lakehouse traffic never touches the public internet.
Authenticate with personal access tokens or service principals. Credentials are encrypted at rest and never logged.
Every query camelAI runs is logged with identity, timestamp, warehouse, and compute cost. Full observability.
Role-based access for your data team. Control who can query, build, publish, or manage connections.
camelAI auto-scales with your SQL warehouse. Serverless, pro, or classic — it adapts to your compute configuration.
What will you build from your lakehouse?
“Connect to our Databricks lakehouse and build an ML model monitoring dashboard. Pull metrics from MLflow — show accuracy, F1 score, and prediction drift over the last 30 days. Add alerts when drift exceeds thresholds.”
Try this prompt“Query our Unity Catalog and build a data catalog browser. Show all schemas, tables, column descriptions, and data lineage. Make it searchable and publish it for the whole data team.”
Try this prompt“Build a Spark job cost tracker from our Databricks SQL warehouse. Show compute costs by team, job duration trends, and flag any queries over $25. Set up a daily cron to email the summary.”
Try this prompt“Create a feature store explorer that connects to our Databricks feature tables. Show feature distributions, freshness, and which models consume each feature. Interactive drill-downs.”
Try this promptWorks with your Databricks stack