Demo Mode

Demo mode provides a quick way to explore SyntheticData without creating a configuration file. It uses sensible defaults to generate a complete synthetic dataset.

Running Demo Mode

datasynth-data generate --demo --output ./demo-output

What Demo Mode Generates

Demo mode creates a comprehensive dataset with:

Demo Configuration

Demo mode uses these defaults:

global:
  industry: manufacturing
  start_date: 2024-01-01
  period_months: 3
  group_currency: USD

companies:
  - code: "1000"
    name: "Demo Company"
    currency: USD
    country: US

chart_of_accounts:
  complexity: medium              # ~400 accounts

transactions:
  target_count: 10000

fraud:
  enabled: true
  fraud_rate: 0.005

anomaly_injection:
  enabled: true
  total_rate: 0.01
  generate_labels: true

output:
  format: csv

Output Structure

After running demo mode, explore the output:

tree demo-output/

demo-output/
├── master_data/
│   ├── chart_of_accounts.csv     # GL accounts
│   ├── vendors.csv               # Vendor master
│   ├── customers.csv             # Customer master
│   ├── materials.csv             # Material/product master
│   └── employees.csv             # Employee/user master
├── transactions/
│   ├── journal_entries.csv       # Main JE file
│   ├── acdoca.csv                # SAP HANA format
│   ├── purchase_orders.csv       # P2P documents
│   ├── goods_receipts.csv
│   ├── vendor_invoices.csv
│   ├── payments.csv
│   ├── sales_orders.csv          # O2C documents
│   ├── deliveries.csv
│   ├── customer_invoices.csv
│   └── customer_receipts.csv
├── subledgers/
│   ├── ar_open_items.csv
│   ├── ap_open_items.csv
│   └── inventory_positions.csv
├── period_close/
│   └── trial_balances/
│       ├── 2024_01.csv
│       ├── 2024_02.csv
│       └── 2024_03.csv
├── labels/
│   ├── anomaly_labels.csv        # For ML training
│   └── fraud_labels.csv
└── controls/
    ├── internal_controls.csv
    └── sod_rules.csv

Exploring the Data

Journal Entries

head -5 demo-output/transactions/journal_entries.csv

Key fields:

• document_id: Unique transaction identifier
• posting_date: When the entry was posted
• company_code: Company identifier
• account_number: GL account
• debit_amount / credit_amount: Entry amounts
• is_fraud: Fraud label (true/false)
• is_anomaly: Anomaly label

Fraud Labels

# View fraud transactions
grep "true" demo-output/labels/fraud_labels.csv | head

Trial Balance

# Check balance coherence
head demo-output/period_close/trial_balances/2024_01.csv

Customizing Demo Output

You can combine demo mode with some options:

# Change output directory
datasynth-data generate --demo --output ./my-demo

# Use demo as starting point, then create config
datasynth-data init --industry manufacturing --complexity medium -o config.yaml
# Edit config.yaml as needed
datasynth-data generate --config config.yaml --output ./output

Use Cases for Demo Mode

Quick Exploration

Test SyntheticData’s capabilities before creating a custom configuration.

Development Testing

Generate test data quickly for development purposes.

Training & Workshops

Provide sample data for training sessions without complex setup.

Benchmarking

Establish baseline performance metrics.

Moving Beyond Demo Mode

When you’re ready for more control:

1. Create a configuration file:

   datasynth-data init --industry <your-industry> -o config.yaml
   

2. Customize settings:

   • Adjust transaction volume
   • Configure multiple companies
   • Enable graph export
   • Fine-tune fraud/anomaly rates

3. Generate with your config:

   datasynth-data generate --config config.yaml --output ./output
   

Next Steps

• Review Quick Start for custom configurations
• Learn the CLI Reference
• Explore Configuration Options
• See Use Cases for real-world examples