Datastrato provides a unified metadata catalog built on Apache Gravitino™ that consolidates metadata from data lakes, warehouses, streaming platforms, and model registries into a single source of truth. The platform enables consistent, cross‑cloud governance policies and reliable, production‑ready data pipelines, supporting AI and GenAI workloads while integrating with existing data tools.
Funding
Funding not disclosed
Founders
Product
Problem
Modern data ecosystems are fragmented across data lakes, warehouses, streaming platforms, and model registries, often spread over multiple cloud providers and regions. Each system maintains its own metadata store and governance rules, leading to inconsistent data definitions, manual governance processes, and fragile pipelines that erode trust in data.
Solution
Datastrato offers a unified metadata catalog built on Apache Gravitino™, a “catalog of catalogs” that consolidates metadata from disparate data and AI workloads into a single source of truth. The platform federates metadata across lakes, warehouses, streaming engines, and ML model registries, enabling consistent governance policies that span clouds and geographic regions. By providing a centralized view of data assets, Datastrato supports reliable, production‑ready data pipelines and empowers AI and GenAI initiatives with trustworthy, well‑governed data. The solution integrates with existing tools and infrastructure, allowing organizations to maintain their current stack while eliminating silos.
Target Audience
Primary customers are data platform and engineering teams in enterprises that manage multi‑cloud data stacks, as well as AI/ML operations groups requiring consistent, governed metadata for model training and deployment.
Features
- Apache Gravitino‑based “catalog of catalogs” that aggregates metadata from heterogeneous data sources
- Cross‑cloud and cross‑region metadata federation for data lakes, warehouses, streaming systems, and model registries
- Centralized governance engine to define and enforce policies uniformly across all data assets
- Single source of truth for data definitions, improving consistency and reducing manual reconciliation
- Native support for AI/GenAI workloads, ensuring production‑ready data quality and lineage
- Seamless integration with existing data tools via standard APIs and connectors