Catalog objects
7
Platform demo · AWS analytics
Lake consumption: S3/Glue inventory with cost proxy
Browse catalog objects and lake landings, then toggle Athena vs Redshift-style consumption to see a scanned-bytes cost proxy on synthetic facts.
How this maps to AWS
AWS data analytics consulting for mid-market teams.
- Catalog inventory with bytes estimates
- Glue/land pipeline stages
- Athena vs Redshift consumption cost proxy
- Synthetic data; not a live AWS account
Related consulting page → · All platform demos · Book My BI Diagnostic
AWS lake → consumption
Landing inventory, catalog objects, and a scanned-bytes cost proxy.
Est. bytes
$21,175,000
Athena cost proxy ($)
0.11
Lens chart A
Lens chart B
Pipeline / job stages
- gold_ops_facts · dbx_job · succeeded · 583s · 8,512 rows
- gold_ops_facts · dbx_job · succeeded · 164s · 7,519 rows
- gold_ops_facts · dbx_job · succeeded · 884s · 3,959 rows
- silver_conform · dbx_job · succeeded · 55s · 5,586 rows
- silver_conform · dbx_job · succeeded · 625s · 6,943 rows
- silver_conform · dbx_job · succeeded · 498s · 6,042 rows
- bronze_normalize · dbx_job · succeeded · 107s · 7,611 rows
- bronze_normalize · dbx_job · succeeded · 519s · 1,073 rows
- bronze_normalize · dbx_job · succeeded · 713s · 3,977 rows
- land_s3_raw · glue · succeeded · 605s · 1,027 rows
- land_s3_raw · glue · succeeded_with_warnings · 621s · 2,074 rows
- land_s3_raw · glue · succeeded · 899s · 2,196 rows
Details
- fact_invoices 3,960,000 B — redshift · KEY(customer_key)
- fact_sales_orders 2,240,000 B — redshift · KEY(customer_key)
- fact_opportunities 630,000 B — snowflake · CLUSTER BY (created_date_key)
- dim_customer 45,000 B — snowflake · AUTO
- gold_ops_mart 4,840,000 B — synapse · HASH(customer_key)
- ext_s3_invoice_landing 7,040,000 B — redshift · EVEN
- vw_secure_revenue 2,420,000 B — snowflake · SECURE VIEW
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 AWS 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 3:32:39 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