Data Mockstar by Adam Mico-Data Generation Tool for All Needs

Example

You specify: domain (e.g., Ecommerce Orders), target rows (e.g., 50,000), and fields such as order_id (UUID), order_date (YYYY-MM-DD with weekday/weekend skew), customer_segment (stratified: 60% Consumer, 30% SMB, 10% Enterprise), country (limited to US/CA/UK), quantity (1–12, long tail), unit_price (normal distribution by category), discount_rate (0–0.5 with 3% heavy discounts), and business rules (order_date ≤ ship_date; if country=US then state is from a US list).

Scenario

A growth analyst is pressure-testing a pricing model. They need realistic, rule-consistent records: no shipments before orders, valid geo hierarchies, and price/discount combinations that resemble reality. Mockstar encodes these constraints, synthesizes data with the requested distributions (normal, uniform, stratified), and ensures relational integrity across linked tables like Customers ↔ Orders ↔ OrderItems. This lets the analyst run scenario analyses (e.g., higher discount volatility in Q4) without risking sensitive data.

Example

Mockstar first returns a 5-row sample to verify headers, types, ranges, and value patterns. You might request: rename total_sale to revenue, change revenue to currency with 2 decimals, tighten age to 18–75 with a small spike at 65, add 2% nulls in email, and inject outliers (top 0.1% revenue above the 99th percentile).

Scenario

A visualization developer needs to confirm that a chart’s conditional formatting behaves correctly when fields are missing, outliers are present, and categories are imbalanced. They iterate on the preview until the sample exhibits all required behaviors (e.g., a handful of extreme values, realistic null patterns, and a dominant category). Once satisfied, they generate the full dataset (default 1,000 rows, or any specified count) and immediately verify that their dashboards render expected alerts, tooltips, and legends.

Example

After approval, you export as CSV for spreadsheets, JSON for APIs, Excel for non-technical stakeholders, SQL for database seeding, or XML for legacy tools. If you later need a different format, you re-export without redesigning the dataset.

Scenario

A QA team seeds a staging database with SQL inserts to test a new reporting service, while the demo team uses the same generated dataset as CSV in a BI tool. Later, a data science colleague requests the exact dataset as JSON to prototype a model input pipeline. Mockstar’s consistent exports enable all teams to work from the same mocked ground truth, reducing rework and preventing format drift.