Revenue YTD
$7,684,087
2026 invoice fact (slicer)
↓ 1% vs 2025
Platform demo · Databricks lakehouse
Lakehouse tables that feed trusted BI—not orphan notebooks
Databricks-certified practitioner framing: bronze/silver/gold habits that land in a semantic model Power BI can consume. Synthetic manufacturer/distributor facts only.
How this maps to Databricks
Databricks consulting with BI handoff (certified practitioner delivery).
- Lakehouse → gold tables → semantic measures
- Trust center patterns for pipeline accountability
- Semantic model as the BI contract after Spark jobs
- Credential claim is separate; this demo is synthetic data
Related consulting page → · All platform demos · Book My BI Diagnostic
Executive KPIs
Leadership snapshot — revenue, margin, backlog, pipeline, and samples from one reconciled model.
Gross margin %
61.5%
Blended (slicer)
↑ 1.0 pts (3 mo vs prior 3)
Open backlog
$2,122,594
Open orders
On-time (open lines)
0%
days late ≤ 0
Pipeline value
$157,899,019
Opportunities
Sample conversion %
31.3%
Samples fact
Revenue & margin % by month Combo
Revenue by channel Donut
Revenue by region & channel Stacked
Top customers
Backlog by aging
Sales & margin
Where volume grows, where mix shifts, and which reps carry the book—with margin in view.
Revenue by month — top channels Stacked area
Revenue (adjusted series)
Revenue by category
Sales rep leaderboard
Region performance
Customer performance
Rank, mix, and concentration—who grows revenue while eroding margin?
Customers: revenue vs. margin % Scatter
Customer rank (revenue)
Revenue by customer type
Sample-to-order
Does sampling activity convert into real revenue—and how fast?
Samples shipped by month
Conversion % by collection
Backlog & fulfillment
Where open demand is building and where fulfillment pressure is rising.
Open order value by month
Fill score by category
CRM ↔ ERP
Pipeline stages and weighted forecast—does CRM tell the same story as orders?
Pipeline by stage
Weighted pipeline by month
Trust center
Refresh status, row counts, and quality checks that keep numbers defensible.
Last refresh
Wednesday, July 29, 2026 11:52:51 PM
Row counts by table
Data quality checks
- Dataset is fully invented (no real companies or personal data).
- Measures follow a star-schema style for realistic drill paths.
- Ask us about mapping these patterns to your own ERP and CRM sources.
Semantic model
Shared tables and certified measures—the foundation for consistent answers and AI-ready Q&A.
Model tables
- dim_date
- dim_region
- dim_channel
- dim_sales_rep
- dim_customer
- dim_product
- fact_invoices
- fact_sales_orders
- fact_inventory_backlog
- fact_samples
- fact_opportunities
- demo_scenario
- data_dictionary
Certified measures
- Total Revenue
- Revenue YTD
- Gross Margin
- Gross Margin %
- Order Value
- Open Backlog
- Pipeline Value
- Weighted Pipeline
- Samples Shipped
- Converted Samples
- Sample Conversion %
Example natural language questions
- What is revenue YTD by channel?
- Which customers grew backlog the most?
- Show margin % trend by month.
- Who are the top reps by pipeline value?
What the numbers are saying
Synthetic dataset for the Databricks platform demo. Revenue and margin trends compare recent periods in this scenario. The point is leadership-ready, reconciled metrics—not a client report or live cloud tenant.
Uses the OpenAI API on synthetic demo numbers only. Public hosts apply rate limits to protect billing—private walkthroughs available on request.