Features
POSIX-compatible file system interface
High-throughput parallel I/O
Data caching and prefetching
Cloud-native deployment (Kubernetes, Docker)
Multi-cloud and hybrid storage support
Data versioning and snapshotting
Fine-grained access control (ACLs, RBAC)
Integration with AI frameworks (PyTorch, TensorFlow)
Multi-tenancy with resource isolation
Real-time monitoring and observability
Self-healing and fault tolerance
High-fidelity synthetic data generation via TabularARGN
Agentic data science automation for training/sampling
Natural language AI Assistant executing Python code
Mock data generation for safe experimentation and testing
Simulated data for edge-case and what-if modeling
Multi-table synthesis with referential integrity
Time-series support and data rebalancing
Differential privacy with temperature control
Deployment on Kubernetes or Red Hat OpenShift
Open-source Synthetic Data SDK under Apache v2 license
Data connectors for Databricks, AWS, Snowflake, BigQuery
Real-time data access from production systems (e.g., Databricks)
Star schema and nested sequences support
REST API and Python Client for programmatic access
Conditional simulation and seeded generation
