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Datastrato Iceberg Lakehouse Platform
What it 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 a standards-compliant Iceberg REST Catalog with metadata caching and table-maintenance functions for production lakehouses. It can register Iceberg, Hive, Glue, relational databases, Kafka, filesets, and models in place without copying metadata. Named Iceberg clients include Spark, Flink, Trino, and Dremio, while the same namespace also resolves across BigQuery. Governance includes catalog-level role-based access, row filtering, column masking, tag-based policies, and audit across engines and agents. S3, GCS, and ADLS storage use short-lived scoped credentials for each principal. Agents can use an MCP metadata interface and GVFS under the same access and audit controls. Compaction, snapshot expiry, and orphan cleanup jobs can dispatch to Airflow, Kubernetes, EMR, or Databricks. Enterprise deployment uses a Helm chart in Kubernetes and a license key; local evaluation uses local Kubernetes. Pricing is on request.
For and against
For
- Standards-compliant Iceberg REST Catalog
- In-place source federation
- Fine-grained governance controls
- Short-lived scoped storage credentials
- Jobs dispatch to major platforms
Against
- Pricing is available on request
- Enterprise deployment requires Kubernetes
- Enterprise activation requires a license key
The verdict
Datastrato Iceberg Lakehouse Platform centralizes federated metadata and access governance without copying source metadata. Its catalog, credential, audit, and maintenance features target enterprise lakehouse environments with diverse clients.
Who it suits
The platform suits enterprise teams and developers managing metadata, governance, lakehouse operations, and AI-ready infrastructure. It is intended for environments connecting multiple data engines, storage systems, and agents.
Plans and prices
Datastrato Enterprise
commercial Apache Gravitino distribution · Kubernetes Helm deployment · license key required · trial keys available
Read 14 Oct 2026 · datastrato.ai
On the record
- AI access
- Agents can use an MCP metadata interface and GVFS for files under the same RBAC, FGAC, and audit controls as other clients.datastrato.ai · read 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.datastrato.ai · read 14 Oct 2026
- Audience
- Datastrato says its platform is for enterprise teams and developers working with metadata management, governance, and AI-ready infrastructure.datastrato.ai · read 5 Oct 2026
- Cloud storage
- Native storage support covers Amazon S3, Google Cloud Storage and Azure Data Lake Storage through URI-based backend dispatch.datastrato.ai · read 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.dev.to · read 14 Oct 2026
- Company headquarters
- LinkedIn lists Datastrato headquarters in San Mateo, California.linkedin.com · read 14 Oct 2026
- Company size
- LinkedIn lists Datastrato as a privately held software-development company with 11–50 employees.linkedin.com · read 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.datastrato.ai · read 14 Oct 2026
- Credential vending
- Short-lived scoped credentials are minted per principal for AWS, GCP and Azure storage access.datastrato.ai · read 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.datastrato.ai · read 5 Oct 2026
- Deployment requirement
- Datastrato Enterprise is deployed as a Helm chart into Kubernetes and activated with a license key.datastrato.ai · read 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.datastrato.ai · read 14 Oct 2026
- Enterprise identity
- Datastrato Enterprise adds SCIM provisioning, local fallback accounts and RBAC across federated catalogs.datastrato.ai · read 14 Oct 2026
- Enterprise lineage
- Enterprise documentation states that it records metadata-operation audits and collects lineage from Spark through OpenLineage.datastrato.ai · read 14 Oct 2026
- Enterprise operations
- The commercial distribution adds a supported Helm chart, health and readiness endpoints, service metrics and an administration UI.datastrato.ai · read 14 Oct 2026
- Enterprise support
- Datastrato Enterprise includes support with a defined response commitment and a maintained release line.datastrato.ai · read 5 Oct 2026
- Federated catalog
- It registers Iceberg, Hive, Glue, relational databases, Kafka, filesets and models in place under one namespace without copying metadata.datastrato.ai · read 14 Oct 2026
- Federation
- It can register Iceberg, Hive, Glue, relational databases, Kafka, filesets, and models in place without copying metadata.datastrato.ai · read 5 Oct 2026
