Practical tools for synthetic data, data contracts, validation, and AI-ready data workflows.
GreatDataLabs builds developer-first open-source projects for teams working with schemas, pipelines, analytics engineering, lakehouse platforms, and responsible AI systems.
Data teams need safe, repeatable systems for testing and design — not just ad hoc scripts.
GreatDataLabs focuses on the engineering layer around modern data systems: schema-first generation, validation before runtime, deterministic testing workflows, open documentation, and tools that fit naturally into notebooks, CI/CD, Spark, lakehouse, and Python development environments.
Featured projects
Open-source tools with practical data-engineering use cases.
Synthetic dataPython · Pandas · Spark
great-generator
A schema-first synthetic data generator for data engineering, QA, analytics, SQL contracts, Spark/lakehouse workflows, query-aware datasets, and deterministic AI-assisted planning.
Generate data from schemas, SQL DDL, JSON Schema, dbt metadata, and data dictionaries.
Support Pandas and Spark workflows with optional Delta/lakehouse paths.
Create relational, CDC, anomaly, dimensional, and Data Vault-style datasets.
Design-time contract validation for Agent Contract Data Modeling, helping teams review schemas, policies, and governance expectations before agentic or automated data workflows run.
Validate contract-style model definitions before implementation.
Support governance-as-code and policy-aware engineering practices.
Fit CI/CD, schema validation, and responsible AI design workflows.