ADF-ish runs
15
Platform demo · Azure data
Pipeline map: ADF/Synapse stages into defensible KPIs
Inspect synthetic ADF-style runs—duration, SLA pressure, and landing-target choice (Azure SQL / Synapse / Fabric-adjacent) before executive consumption.
How this maps to Azure
Azure data consulting for mid-market BI and analytics.
- Pipeline stage durations and row throughput
- SLA breach flags on long runs
- Landing-target switch rewriting the handoff story
- Synthetic scenario; not a live Azure subscription
Related consulting page → · All platform demos · Book My BI Diagnostic
Azure pipeline map
ADF / Synapse-style stages with duration and row throughput.
Avg duration (sec)
477
SLA breaches
3
Lens chart A
Lens chart B
Pipeline / job stages
- bi_dataset_publish · adf · succeeded · 593s · 3,039 rows
- bi_dataset_publish · adf · succeeded · 273s · 3,265 rows
- bi_dataset_publish · adf · succeeded · 254s · 1,888 rows
- semantic_refresh · adf · succeeded · 458s · 6,498 rows
- semantic_refresh · adf · succeeded · 579s · 9,237 rows
- semantic_refresh · adf · succeeded · 824s · 4,866 rows
- synapse_load · adf · succeeded · 238s · 10,028 rows
- synapse_load · adf · succeeded · 883s · 6,235 rows
- synapse_load · adf · succeeded · 514s · 6,888 rows
- ingest_crm_opps · adf · succeeded · 323s · 6,858 rows
- ingest_crm_opps · adf · succeeded · 348s · 8,894 rows
- ingest_crm_opps · adf · succeeded · 801s · 6,694 rows
- ingest_erp_orders · adf · succeeded · 84s · 4,997 rows
- ingest_erp_orders · adf · succeeded · 557s · 3,016 rows
- ingest_erp_orders · adf · succeeded · 429s · 8,597 rows
Executive KPIs
Leadership snapshot — revenue, margin, backlog, pipeline, and samples from one reconciled model.
Revenue YTD
$12,729,420
2026 invoice fact (slicer)
↓ 3% vs 2025
Gross margin %
54.8%
Blended (slicer)
↓ 1.3 pts (3 mo vs prior 3)
Open backlog
$4,977,044
Open orders
On-time (open lines)
0%
days late ≤ 0
Pipeline value
$368,361,808
Opportunities
Sample conversion %
31.5%
Samples fact
Revenue & margin % by month Combo
Revenue by channel Donut
Revenue by region & channel Stacked
Top customers
Backlog by aging
What the numbers are saying
Synthetic dataset for the Azure 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.
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
Monday, September 28, 2026 3:33:40 AM
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.
- Quality fail · orphan_crm_account: 26 rows (high).
- Quality fail · orphan_erp_customer: 31 rows (high).
- Quality fail · fuzzy_key_confidence: 33 rows (medium).
- Quality fail · negative_margin_lines: 1876 rows (medium).
- Quality fail · stale_backlog_31plus: 3030 rows (high).
- Quality fail · opp_without_erp_link: 1011 rows (medium).
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
- bridge_crm_erp_keys
- fact_data_quality_events
- fact_pipeline_runs
- dim_semantic_measure
- fact_medallion_stats
- dim_warehouse_object
- 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 is the CRM↔ERP match rate?
How to present this