Realistic synthetic data.
Zero production risk.
Generate relationally consistent test data directly from your SQL schema. Paste your DDL, configure the rules, and export to CSV, JSON, or SQL.
CREATE TABLE customer (
id INTEGER PRIMARY KEY,
email VARCHAR(255) NOT NULL UNIQUE,
loyalty_tier VARCHAR(10)
CHECK (loyalty_tier IN ('bronze','silver','gold')),
created_at TIMESTAMP NOT NULL
);
CREATE TABLE orders (
id INTEGER PRIMARY KEY,
customer_id INTEGER NOT NULL REFERENCES customer (id),
total DECIMAL(10,2) CHECK (total > 0),
status VARCHAR(20)
);# customer.csv
id,email,loyalty_tier,created_at
1,amelia.hughes@example.com,gold,2024-01-12T09:14:02
2,mateo.rossi@example.com,silver,2024-01-15T17:42:55
3,priya.nair@example.com,gold,2024-02-03T11:08:31
# orders.csv (customer_id always points at a real customer)
id,customer_id,total,status
101,1,128.40,delivered
102,3,64.99,shipped
103,1,219.00,paidWhy not just use production data?
Anonymizing real data is slow, risky, and still leaves gaps in your test coverage. Hand-crafted mock data doesn't scale.
The Old Way
- Compliance & Privacy RisksCopying customer data to local machines or staging environments violates GDPR, SOC2, and HIPAA.
- Broken Relational IntegrityFake data scripts often generate random IDs, leading to missing parents, orphaned children, and broken UI state.
The Skogi Way
- 100% Safe to ShareZero real customer information. You can confidently commit it to git, use it in CI/CD, or share with external contractors.
- Perfectly RelationalGenerates a complete graph. If an order has a `user_id`, you can be certain that user exists with a realistic name and email.
Built for realism. Not randomness.
Skogi understands your schema, preserves relationships, and generates data that behaves like real data.
Relational integrity
Every foreign key points to a real parent record.
Skogi automatically infers table relationships and generates parents before children, so every customer, order, invoice, and payment stays perfectly connected.
Fine-grained control
Dial in every column when the defaults aren't enough.
- Recognize business meaning
Tag any column as a full name, email, phone, or IP — 20+ types.
- Generation rules
Dial null rates and value spread, one column at a time.
- Calculated values stay consistent
Order totals always equal the sum of their line items.
- Temporal ordering
Events happen in chronological order—from account creation to modification to deletion.
Industry packs
Generate banking, e-commerce, healthcare, logistics, and payments datasets with realistic values for columns like IBAN, NPI, or SKU.
Realistic distributions
Prices follow log-normal curves. Ages cluster realistically. Null values appear naturally instead of randomly.
Deterministic seeds
Reproduce exactly the same dataset with a single seed—perfect for debugging, regression tests, and sharing bugs with teammates.
Inside the Workspace
Three steps from a schema to a downloadable dataset.
-- Paste your DDL here
CREATE TABLE users (
id UUID PRIMARY KEY,
email VARCHAR UNIQUE
);
CREATE TABLE orders (
id UUID PRIMARY KEY,
user_id UUID REFERENCES users(id),
amount DECIMAL
);Who is it for?
If you need data that behaves like the real thing, this is for you.
Build features, not seed scripts.
Stop maintaining brittle seed scripts that break every time your schema changes. Generate a fresh, realistic database in seconds to test new features or reproduce bugs locally.
Generate your first dataset today
Start free — no production data required. Upgrade when you need more rows, more packs, and SQL output.