DataHub vs Datastrato Iceberg Lakehouse Platform
DataHub
datahub.com
Datastrato Iceberg Lakehouse Platform
datastrato.aiSide by side
| Figure | DataHub | Datastrato Iceberg Lakehouse Platform |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | Yes | Yes |
| Open source | Yes | Yes |
| Web | Yes | — |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 1 |
| Spec score · Active Metadata Management Software | 8.8 (#2) | 8.3 (#6) |
| Company headquarters | LinkedIn lists the headquarters at 3101 Park Boulevard, Palo Alto, California 94306, United States. | LinkedIn lists Datastrato headquarters in San Mateo, California. |
| Company size | LinkedIn lists DataHub as a privately held software-development company with 51–200 employees. | LinkedIn lists Datastrato as a privately held software-development company with 11–50 employees. |
| Documentation depth | The documentation site includes integration guides, API and SDK references, deployment, authentication, authorization, developer and release-history sections. | The Enterprise documentation is organized into installation, metadata exploration, governance, automation, source connections, authorization, monitoring, configuration, engines, agents, reference and release notes. |
| Founded | LinkedIn and Gartner list DataHub's founding year as 2021. | LinkedIn lists Datastrato's founding year as 2023. |
| Governance | DataHub includes a versioned business glossary, ownership and stewardship tools, access policies and compliance controls. | RBAC, row filtering, column masking and tag-based policies are evaluated at the catalog for every request. |
| Integrations | The project advertises more than 150 integrations, including Snowflake, BigQuery, dbt, Databricks, Looker, Tableau, Power BI, Airflow, Kafka, S3 and Iceberg. | The product page names Spark, Flink, Trino, and Dremio as Iceberg catalog clients, and says the same namespace also resolves across BigQuery. |
| Purpose | DataHub describes its platform as a context platform that unifies technical metadata, business knowledge, and documentation for enterprise data and AI agents. | Datastrato is a federated metadata catalog built on Apache Gravitino and Iceberg REST Catalog for connecting data sources and governing access across clients. |
| Support | Open-source users receive community Slack and self-service documentation, while Cloud customers receive dedicated customer success and private support channels. | Standard and Premium support include a dedicated Slack Connect channel, named technical account manager and one-hour P0 response, with 24/7 response on Premium. |
| System requirements | The quickstart requires Docker and Docker Compose v2, Python 3.10 or newer, and a tested allocation of 2 CPUs, 8 GB RAM, 2 GB swap and 13 GB disk. | Published minimums are Kubernetes 1.29, Helm 3.8, two CPU cores, four GiB memory and a default storage class. |
| What it does | DataHub is an AI and data context platform that unifies enterprise data discovery, governance and observability. | Datastrato provides a federated metadata catalog and Iceberg REST platform for governing data and AI assets across clouds, engines and formats. |
Plans
Datastrato Iceberg Lakehouse Platform
- Datastrato EnterprisePriced on request
In their own words
DataHub
DataHub brings technical metadata, business knowledge, and documentation together for enterprise data and AI agents. It is available as self-hosted Core or as managed DataHub Cloud. Cloud provides natural-language search, an Ask DataHub chat agent, smart ranking, and a hosted MCP server that connects AI tools to its catalog. Its observability tools run automated schema, freshness, volume, and custom quality checks, with AI anomaly detection and incident workflows. Cross-platform, column-level lineage traces data from sources through transformations and AI models to downstream assets. DataHub Cloud has more than 100 pre-built connectors and names Slack, Microsoft Teams, Chrome, and BI tools among its native integrations. Cloud is described as SOC 2 compliant, with role- and attribute-based access controls and an in-VPC execution option for sensitive sources. Cloud availability is described as SLA-backed at 99.5%. DataHub Core is free to deploy, but requires users to handle installation, configuration, upgrades, uptime, and troubleshooting. Cloud pricing depends on data volume, users, and selected capabilities. A Google Cloud offer advertises a 21-day Cloud trial with a dedicated instance and full platform access.
Datastrato Iceberg Lakehouse Platform
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 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. Spark, Flink, Trino, and Dremio are named Iceberg catalog clients, and the same namespace resolves across BigQuery. Governance includes catalog-level role-based access, row filtering, column masking, tag-based policies, and auditing across engines and agents. S3, GCS, and ADLS are supported with short-lived scoped credentials per principal. Agents can use an MCP metadata interface and GVFS under the same access controls. Per-table compaction, snapshot expiry, and orphan cleanup jobs can be sent to Airflow, Kubernetes, EMR, or Databricks. Enterprise is installed with a Helm chart in Kubernetes and activated by license key. Pricing is on request; the commercial distribution includes support with a defined response commitment and maintained release line.