Guides

Deep-dive articles on specific use cases and comparisons.

Workflows#

• dbt seed integration: generate seeds from your project's own schema.yml; dbt build passes on day zero
• Pytest fixtures: realistic relational fixtures with zero configuration via pytest-misata
• MCP server: use Misata from Claude, Cursor, and other AI agents
• Database seeding in Python
• REST API
• Jupyter notebooks
• Streaming generation

Techniques#

• Outcome curves: declare the aggregate, generate rows that hit it exactly
• Lexicons, motifs and joint margins: vocabulary that keeps growing, patterns that exist on purpose, several margins that hold at once
• Rate curves: declare a rate over time and hit it exactly
• Distributions: the shape of a numeric column, stated
• Conditional columns: a column whose values depend on another
• Mimic mode: privacy-safe synthetic twins of CSVs you already have
• From DDL: generate straight from CREATE TABLE statements
• Correlations
• Anomaly injection
• Geospatial data
• Long-form text
• Narrative patterns

Use cases and comparisons#

• Faker vs SDV vs Misata
• Multi-table synthetic data
• Synthetic data for BI demos
• Research benchmarks