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Datastrato Iceberg Lakehouse Platform
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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 a standards-compliant Iceberg REST Catalog with 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. Its named catalog clients include Spark, Flink, Trino, and Dremio, and the same namespace also resolves across BigQuery.
Governance features include catalog-level role-based access, row filtering, column masking, tag-based policies, and audit across engines and agents. The platform supports S3, GCS, and ADLS, with short-lived scoped credentials issued per principal. Agents can use an MCP metadata interface and GVFS for files under the same access and audit controls. Per-table compaction, snapshot expiry, and orphan cleanup jobs can be dispatched to Airflow, Kubernetes, EMR, or Databricks. Datastrato Enterprise is installed from a Helm chart into a Kubernetes cluster and activated with a license key; local evaluation uses local Kubernetes. The paid Enterprise plan is priced on request. The company states SOC 2 Type II, ISO 27001 readiness, HIPAA BAA, and GDPR alignment.
Who it suits
Datastrato describes the platform for enterprise teams and developers working on metadata management, governance, and AI-ready infrastructure. It may fit organizations that need to connect multiple data systems while applying shared access controls and catalog governance across clients and agents.
What it does well
- Registers multiple data sources in place without copying metadata.
- Supports Spark, Flink, Trino, Dremio, and BigQuery namespaces.
- Includes row filtering, column masking, and tag-based policies.
- Supports S3, GCS, and ADLS with scoped credentials.
- Maintenance jobs can be dispatched to Airflow, Kubernetes, EMR, or Databricks.
- Enterprise includes support with a defined response commitment.
What to know first
- Enterprise pricing is available on request.
- Deployment requires a Kubernetes cluster and license key.
- Local evaluation also uses local Kubernetes.
- Audit reports and control documentation require NDA-gated access.
Limits
The listed Enterprise plan has pricing on request and requires deployment with a license key. Audit reports and control documentation require NDA-gated access.
Plans
| Plan | Billed and limits | Price |
|---|---|---|
| Datastrato Enterprise | commercial Apache Gravitino distribution · Kubernetes Helm deployment · license key required · trial keys available · supported release line | On request |
As datastrato.ai lists them, read 14 Oct 2026. The maker's own page has the final word.
Getting started
- Contact Datastrato for Enterprise pricing.
- Prepare a Kubernetes cluster, or use local Kubernetes for evaluation.
- Install Datastrato Enterprise from its Helm chart.
- Activate the installation with a license key.
- Connect supported data sources and catalog clients.
Verdict
Choose Datastrato if your team needs a federated Iceberg catalog with governance spanning data clients, storage, and agent access. Its enterprise deployment and on-request pricing make it a better fit for organizations prepared to evaluate and run a Kubernetes-based platform.
Questions
How is Datastrato Enterprise priced?
Pricing is on request; the listed Enterprise plan does not state a price.
How is it deployed?
Datastrato Enterprise is installed from a Helm chart into Kubernetes and activated with a license key. Local evaluation uses local Kubernetes.
Which data sources can it register?
It can register Iceberg, Hive, Glue, relational databases, Kafka, filesets, and models in place.
Which clients and integrations are named?
The product page names Spark, Flink, Trino, and Dremio as Iceberg catalog clients. It also says the same namespace resolves across BigQuery.
Which cloud storage systems does it support?
It supports S3, GCS, and ADLS, with short-lived scoped credentials vended per principal.
What compliance and security details does Datastrato state?
Its homepage states SOC 2 Type II, ISO 27001 readiness, HIPAA BAA, and GDPR alignment. Audit reports and control documentation require NDA-gated access.
Facts from Datastrato Iceberg Lakehouse Platform's own pages
- 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, 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, 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, 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, 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, 14 Oct 2026
- Company headquarters
- LinkedIn lists Datastrato headquarters in San Mateo, California. · linkedin.com, 14 Oct 2026
- Company size
- LinkedIn lists Datastrato as a privately held software-development company with 11–50 employees. · linkedin.com, 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, 14 Oct 2026
- Credential vending
- Short-lived scoped credentials are minted per principal for AWS, GCP and Azure storage access. · datastrato.ai, 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, 5 Oct 2026
- Deployment requirement
- Datastrato Enterprise is deployed as a Helm chart into Kubernetes and activated with a license key. · datastrato.ai, 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, 14 Oct 2026
- Enterprise identity
- Datastrato Enterprise adds SCIM provisioning, local fallback accounts and RBAC across federated catalogs. · datastrato.ai, 14 Oct 2026
- Enterprise lineage
- Enterprise documentation states that it records metadata-operation audits and collects lineage from Spark through OpenLineage. · datastrato.ai, 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, 14 Oct 2026
- Enterprise support
- Datastrato Enterprise includes support with a defined response commitment and a maintained release line. · datastrato.ai, 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, 14 Oct 2026
- Federation
- It can register Iceberg, Hive, Glue, relational databases, Kafka, filesets, and models in place without copying metadata. · datastrato.ai, 5 Oct 2026
- Founded
- LinkedIn lists Datastrato's founding year as 2023. · linkedin.com, 14 Oct 2026
- Founder
- Datastrato's origin-story page identifies Junping Du as founder and CEO. · datastrato.ai, 14 Oct 2026
- GitHub activity
- The Apache Gravitino repository shows about 3.2k stars and 954 forks. · github.com, 14 Oct 2026
- Governance
- RBAC, row filtering, column masking and tag-based policies are evaluated at the catalog for every request. · datastrato.ai, 14 Oct 2026
- High availability
- Metadata persistence can use PostgreSQL or MySQL through a pluggable JDBC backend with disaster-recovery support. · datastrato.ai, 14 Oct 2026
- Iceberg platform
- Its Iceberg Lakehouse Platform provides a standards-compliant Iceberg REST Catalog with metadata caching and table maintenance for production lakehouses. · datastrato.ai, 5 Oct 2026
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