Demo Datasets

Atlas ships with three demo datasets for evaluation and development.

Quick Comparison

Use Simple when you want a fast setup. Use Cybersec when you want to see how Atlas handles a real-world B2B SaaS database with messy data. Use E-commerce when you want a universally understood domain (orders, products, customers) at production scale.

Simple Demo (default)

Three clean tables: companies (50), people (~200), accounts (80). No tech debt, no ambiguity.

Note: Bare --demo (with no argument) defaults to simple. If you already ran bun run db:up, the simple demo data is already seeded -- just run bun run atlas -- init (without --demo) to profile it. The --demo flag is for when you have not run db:up or want to explicitly re-seed.

# Option A: Using db:up (already seeds simple demo)
bun run db:up
bun run atlas -- init

# Option B: Explicit seed (without db:up, or to re-seed)
bun run atlas -- init --demo

Suggested Questions

Try these in the Atlas chat UI to exercise different patterns:

Aggregation:

• "How many companies are there by industry?"
• "Which industries have the most accounts?"

Joins:

• "Who are the top 5 people by account value?"
• "Show me all people at companies in the Technology industry"

Filtering:

• "List all companies with more than 3 accounts"
• "Which people are associated with accounts created in the last year?"

Cybersec Demo: Sentinel Security

A 62-table B2B cybersecurity SaaS company database. ~500K rows spanning 2019-2025. Covers vulnerability management, threat detection, compliance, billing, and reporting.

Loading

Requires PostgreSQL (uses GENERATE_SERIES for data generation). Note that bun run db:up only seeds the simple demo -- the --demo cybersec flag seeds the cybersec dataset on top.

# Start local Postgres (if not already running)
bun run db:up

# Load cybersec demo and generate semantic layer
bun run atlas -- init --demo cybersec

# Start Atlas
bun run dev

To reset and reload from scratch:

bun run db:reset
bun run atlas -- init --demo cybersec

Suggested Questions

Try these in the Atlas chat UI to exercise different patterns:

Basic aggregation:

• "How many vulnerabilities by severity?"
• "What's the total invoice amount by organization?"
• "How many scans ran in the last 30 days?"

Joins:

• "Which organizations have the most critical scan results?"
• "Show me the top 10 users by number of alerts acknowledged"
• "Which compliance frameworks have the most failing controls?"

Time series:

• "What's the trend in critical vulnerabilities over the past 6 months?"
• "Show me monthly invoice totals"

Aggregation + filtering:

• "What's the average time to remediate by severity level?"
• "Alert noise ratio: what percentage of alerts become incidents?"

Tech debt discovery (exercises profiler warnings):

• "Break down organizations by industry" (surfaces enum inconsistency via profiler note)
• "Show me scan results for assets that no longer exist" (orphan rows)
• "What tables exist that look abandoned?" (agent reads profiler_notes)
• "Compare scan_results_denormalized with scan_results" (denormalized flag)

Tech Debt Patterns

The cybersec dataset was designed to include four real-world tech debt patterns that the profiler detects automatically:

1. Missing FK Constraints

Eight *_id columns reference other tables but lack FOREIGN KEY constraints. The profiler infers these from naming conventions and marks them with inferred: true in the generated YAML.

2. Abandoned Tables

Six tables match legacy/temp naming patterns and have no inbound foreign keys:

• old_scan_results_v2 -- abandoned schema migration
• temp_asset_import_2024 -- one-time CSV import artifact
• feature_flags_legacy -- replaced by LaunchDarkly
• notifications_backup -- migration backup
• user_sessions_archive -- old session system
• legacy_risk_scores -- old risk scoring algorithm

The profiler flags these with possibly_abandoned and prepends a warning in use_cases.

3. Inconsistent Enums

Some text columns have case-inconsistent values:

• organizations.industry: 'Technology', 'tech', 'Tech', 'TECHNOLOGY'
• compliance_findings.status: 'pass', 'Pass', 'PASS'

The profiler detects these and adds LOWER() guidance in the glossary.

4. Denormalized Tables

Four reporting/cache tables duplicate data from other tables:

• scan_results_denormalized -- pre-joined scan results
• daily_scan_stats -- daily rollup
• monthly_vulnerability_summary -- monthly aggregates
• executive_dashboard_cache -- pre-computed dashboard data

The profiler flags these with possibly_denormalized.

Schema Overview

Table groups:

• Core Business (7 tables): organizations, users, teams, roles
• Billing (6 tables): plans, subscriptions, invoices
• Asset Management (6 tables): assets, agents, agent_heartbeats
• Vulnerability Management (7 tables): vulnerabilities, scans, scan_results
• Threat & Incident (6 tables): incidents, alerts
• Threat Intelligence (3 tables): threat_feeds, IOCs, threat_actors
• Compliance (4 tables): frameworks, controls, assessments, findings
• Product Usage (5 tables): API keys, requests, feature usage, login events
• Reporting (5 tables): denormalized/rollup tables
• Reports & Dashboards (4 tables): saved reports, dashboards
• Integration & Audit (3 tables): integrations, audit_log
• Legacy (6 tables): abandoned tables

E-commerce Demo: NovaMart

A 52-table DTC (direct-to-consumer) home goods brand database. ~480K rows spanning 2020-2025. NovaMart was founded during the pandemic, started with bedding, expanded to kitchen/bath/outdoor, and launched a small marketplace in 2022.

Loading

Requires PostgreSQL (uses GENERATE_SERIES for data generation). Note that bun run db:up only seeds the simple demo -- the --demo ecommerce flag seeds the ecommerce dataset on top.

# Start local Postgres (if not already running)
bun run db:up

# Load e-commerce demo and generate semantic layer
bun run atlas -- init --demo ecommerce

# Start Atlas
bun run dev

To reset and reload from scratch:

bun run db:reset
bun run atlas -- init --demo ecommerce

Suggested Questions

Try these in the Atlas chat UI to exercise different patterns:

Sales & revenue:

• "What's the monthly revenue trend since launch?"
• "Top 10 products by total revenue"
• "Average order value by customer segment"
• "Revenue breakdown: own products vs marketplace"

Customer analytics:

• "How many customers are in each loyalty tier?"
• "What's the customer retention rate by cohort?"
• "Breakdown of new vs returning customers per month"
• "Average customer lifetime value by acquisition source"

Operations:

• "Average delivery time by carrier"
• "Return rate by product category"
• "Top reasons for returns"
• "Shipping cost per order over time"

Marketing:

• "Which UTM sources drive the most revenue?"
• "Email campaign conversion rates"
• "Promo code usage rate by campaign"

Tech debt discovery (exercises profiler warnings):

• "Why are there two price fields on products?" (schema evolution)
• "Break down customers by acquisition source" (surfaces enum inconsistency)
• "What tables look abandoned?" (agent reads profiler_notes)
• "Compare orders_denormalized with orders" (denormalized flag)

Tech Debt Patterns

The e-commerce dataset includes the same four tech-debt patterns as the cybersec demo (missing FK constraints, abandoned tables, inconsistent enums, denormalized tables). E-commerce-specific examples:

• 19 missing FK constraints -- plus ~1.5% of payments reference nonexistent orders (orphaned from deleted test orders)
• 4 abandoned tables -- old_orders_v1, temp_product_import_2023, legacy_analytics_events, payment_methods_backup
• Inconsistent enums -- e.g. customers.acquisition_source: 'Google'/'google'/'GOOGLE'; loyalty_accounts.tier: 'Gold'/'gold'/'GOLD'
• 5 denormalized tables -- orders_denormalized, daily_sales_summary, monthly_revenue_summary, product_performance_cache, customer_ltv_cache

Schema Evolution Artifacts

The dataset includes five schema evolution instances where old and new columns coexist:

Schema Overview

Table groups:

• Core Commerce (6 tables): customers, addresses, segments, loyalty
• Product Catalog (7 tables): products, variants, images, tags, inventory
• Marketplace (4 tables): sellers, applications, payouts, performance
• Orders & Transactions (7 tables): orders, items, events, payments, refunds, gift cards
• Shipping & Fulfillment (5 tables): shipments, carriers, returns
• Marketing & Promotions (5 tables): promotions, email campaigns, UTM tracking
• Reviews (3 tables): product reviews, responses, helpfulness
• Reporting (5 tables): denormalized/rollup/cache tables
• Site Analytics (3 tables): page views, cart events, search queries
• Internal / Ops (3 tables): admin users, audit log, settings
• Legacy (4 tables): abandoned tables