# Guides Deep-dive articles on specific use cases and comparisons. ## Workflows - [dbt seed integration](dbt-seed.md): generate seeds from your project's own schema.yml; dbt build passes on day zero - [Pytest fixtures](testing.md): realistic relational fixtures with zero configuration via pytest-misata - [MCP server](mcp.md): use Misata from Claude, Cursor, and other AI agents - [Database seeding in Python](database-seeding-python.md) - [REST API](rest-api.md) - [Jupyter notebooks](jupyter.md) - [Streaming generation](streaming.md) ## Techniques - [Outcome curves](outcome_curves.md): declare the aggregate, generate rows that hit it exactly - [Lexicons, motifs and joint margins](declared-vocabulary-and-structure.md): vocabulary that keeps growing, patterns that exist on purpose, several margins that hold at once - [Rate curves](rate_curves.md): declare a rate over time and hit it exactly - [Distributions](distributions.md): the shape of a numeric column, stated - [Conditional columns](conditional-columns.md): a column whose values depend on another - [Mimic mode](mimic.md): privacy-safe synthetic twins of CSVs you already have - [From DDL](from-ddl.md): generate straight from CREATE TABLE statements - [Correlations](correlations.md) - [Anomaly injection](anomaly-injection.md) - [Geospatial data](geospatial.md) - [Long-form text](long-form-text.md) - [Narrative patterns](narrative-patterns.md) ## Use cases and comparisons - [Faker vs SDV vs Misata](faker-vs-sdv-vs-misata.md) - [Multi-table synthetic data](multi-table-synthetic-data.md) - [Synthetic data for BI demos](synthetic-data-for-bi-demos.md) - [Research benchmarks](research-benchmarks.md)