Features
Lakehouse architecture unifying data and AI
Lakebase: serverless Postgres database for AI apps
Agent Bricks: build production AI agents grounded in data
AI/BI Genie: natural language analytics and dashboards
Genie One, Genie Agents, Genie Ontology (June 2026)
Lakehouse//RT: real-time performance layer (June 2026)
CustomerLake: agentic CDP (June 2026)
Unity Catalog: unified governance for data and AI
Lakeflow: ETL for batch and streaming
Serverless data warehousing with Photon engine
Delta Lake: ACID transactions on data lakes
MLflow: ML lifecycle management
Collaborative notebooks (Python, SQL, R, Scala)
Multi-cloud support: AWS, Azure, GCP
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
