Datastrato Iceberg Lakehouse Platform
Datastrato Iceberg Lakehouse Platform is a federated metadata catalog for connecting data sources and governing access across clients.
What Datastrato Iceberg Lakehouse Platform is
Datastrato Iceberg Lakehouse Platform is a federated metadata catalog for connecting data sources and governing access across clients. Built on Apache Gravitino and Iceberg REST Catalog, it provides metadata caching and table maintenance for production lakehouses. It can register Iceberg, Hive, Glue, relational databases, Kafka, filesets, and models in place without copying metadata. Named catalog clients include Spark, Flink, Trino, and Dremio, with the same namespace also resolving across BigQuery. Governance includes role-based catalog access, row filtering, column masking, tag-based policies, and audit across engines and agents. It supports S3, GCS, and ADLS with scoped, short-lived credentials per principal. Agents can access metadata and files under the same controls. Table compaction, snapshot expiry, and orphan cleanup jobs can be dispatched to Airflow, Kubernetes, EMR, or Databricks. Enterprise deployment uses a Helm chart in Kubernetes and a license key; local evaluation also uses Kubernetes. Pricing is on request.
Good
- Registers multiple source types without copying metadata
- Supports role-based access, filtering, masking, and audit
- Works with S3, GCS, and ADLS
- Maintenance jobs can dispatch to several platforms
- Includes enterprise support with a response commitment
Watch out
- Pricing is on request
- Enterprise deployment requires Kubernetes and a license key
- Audit reports and control documents require NDA-gated access
Who it suits
Datastrato identifies enterprise teams and developers working on metadata management, governance, and AI-ready infrastructure as its audience. It is relevant to organizations connecting multiple data sources and enforcing shared access controls across clients.
Verdict
This platform combines data-source federation, lakehouse maintenance, and governance controls across clients. Its Kubernetes deployment and request-based pricing are important considerations for teams evaluating it.
Plans and prices
From Datastrato Iceberg Lakehouse Platform's own pricing page, checked 14 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Datastrato Enterprise | Priced on request | commercial Apache Gravitino distribution · Kubernetes Helm deployment · license key required · trial keys available · supported release line |
The facts
Each one read from Datastrato Iceberg Lakehouse Platform's own pages, with the date it was checked.
What it does
- AI access
- Agents can use an MCP metadata interface and GVFS for files under the same RBAC, FGAC, and audit controls as other clients.checked 5 Oct 2026
- AI agents
- The product exposes governed metadata through MCP and unstructured files through GVFS, with the same policy and audit path used by human clients.checked 14 Oct 2026
- Audience
- Datastrato says its platform is for enterprise teams and developers working with metadata management, governance, and AI-ready infrastructure.checked 5 Oct 2026
- Cloud storage
- Native storage support covers Amazon S3, Google Cloud Storage and Azure Data Lake Storage through URI-based backend dispatch.checked 14 Oct 2026
- Community history
- A DEV Community introduction says Gravitino was initiated by Datastrato, open-sourced in 2023, donated to Apache Incubator in 2024 and graduated to an Apache Top-Level Project in May 2025.checked 14 Oct 2026
- Compliance claims
- Datastrato states SOC 2 Type II, ISO 27001 readiness, HIPAA BAA and GDPR alignment, with audit reports available under NDA.checked 14 Oct 2026
- Credential vending
- Short-lived scoped credentials are minted per principal for AWS, GCP and Azure storage access.checked 14 Oct 2026
- Deployment
- Datastrato Enterprise is installed from a Helm chart into a Kubernetes cluster and activated with a license key; local evaluation uses local Kubernetes.checked 5 Oct 2026
- Deployment requirement
- Datastrato Enterprise is deployed as a Helm chart into Kubernetes and activated with a license key.checked 14 Oct 2026
- Documentation depth
- The Enterprise documentation is organized into installation, metadata exploration, governance, automation, source connections, authorization, monitoring, configuration, engines, agents, reference and release notes.checked 14 Oct 2026
- Enterprise identity
- Datastrato Enterprise adds SCIM provisioning, local fallback accounts and RBAC across federated catalogs.checked 14 Oct 2026
- Enterprise lineage
- Enterprise documentation states that it records metadata-operation audits and collects lineage from Spark through OpenLineage.checked 14 Oct 2026
- Enterprise operations
- The commercial distribution adds a supported Helm chart, health and readiness endpoints, service metrics and an administration UI.checked 14 Oct 2026
- Enterprise support
- Datastrato Enterprise includes support with a defined response commitment and a maintained release line.checked 5 Oct 2026
- Federated catalog
- It registers Iceberg, Hive, Glue, relational databases, Kafka, filesets and models in place under one namespace without copying metadata.checked 14 Oct 2026
- Federation
- It can register Iceberg, Hive, Glue, relational databases, Kafka, filesets, and models in place without copying metadata.checked 5 Oct 2026
- Founder
- Datastrato's origin-story page identifies Junping Du as founder and CEO.checked 14 Oct 2026
- GitHub activity
- The Apache Gravitino repository shows about 3.2k stars and 954 forks.checked 14 Oct 2026
- Governance
- RBAC, row filtering, column masking and tag-based policies are evaluated at the catalog for every request.checked 14 Oct 2026
- High availability
- Metadata persistence can use PostgreSQL or MySQL through a pluggable JDBC backend with disaster-recovery support.checked 14 Oct 2026
The company
- Company headquarters
- LinkedIn lists Datastrato headquarters in San Mateo, California.checked 14 Oct 2026
- Company size
- LinkedIn lists Datastrato as a privately held software-development company with 11–50 employees.checked 14 Oct 2026
- Founded
- LinkedIn lists Datastrato's founding year as 2023.checked 14 Oct 2026
Where it runs
- Iceberg platform
- Its Iceberg Lakehouse Platform provides a standards-compliant Iceberg REST Catalog with metadata caching and table maintenance for production lakehouses.checked 5 Oct 2026
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