Misata – synthetic data engine using LLM and Vectorized NumPy
Hey HN, I’m the author. I built Misata because existing tools (Faker, Mimesis) are great for random rows but terrible for relational or temporal integrity. I needed to generate data for a dashboard where "Timesheets" must happen after "Project Start Date," and I wanted to define these rules via natural language. How it works: LLM Layer: Uses Groq/Llama-3.3 to parse a "story" into a JSON schema constraint config. Simulation Layer: Uses Vectorized NumPy (no loops) to generate data. It builds a DAG of tables to ensure parent rows exist before child rows (referential integrity).…
In plain words
Misata is a synthetic data generation tool that uses large language models and NumPy to create realistic datasets with relational and temporal constraints. Users define data rules through natural language descriptions, which the tool converts into schema configurations. It generates data by building a directed acyclic graph of tables to maintain referential integrity, ensuring parent records exist before child records. The tool performs vectorized operations without loops for speed and can generate approximately 250,000 rows per second. It targets developers and data engineers who need test data that respects complex business logic rather than random, disconnected information.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN, I’m the author. I built Misata because existing tools (Faker, Mimesis) are great for random rows but terrible for relational or temporal integrity. I needed to generate data for a dashboard where "Timesheets" must happen after "Project Start Date," and I wanted to define these rules via natural language. How it works: LLM Layer: Uses Groq/Llama-3.3 to parse a "story" into a JSON schema constraint config. Simulation Layer: Uses Vectorized NumPy (no loops) to generate data. It builds a DAG of tables to ensure parent rows exist before child rows (referential integrity). Performance: Generates ~250k rows/sec on my M1 Air. It’s early alpha. The "Graph Reverse Engineering" (describe a chart -> get data) is experimental but working for simple curves. pip install misata I’d love feedback on the simulator.py architecture—I’m currently keeping data in-memory (Pandas) which hits a ceiling at ~10M rows. Thinking of moving to DuckDB for out-of-core generation next. Thoughts?
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