Datastrato Iceberg Lakehouse Platform vs Marquez
Datastrato Iceberg Lakehouse Platform
datastrato.aiMarquez
marquezproject.aiSide by side
| Figure | Datastrato Iceberg Lakehouse Platform | Marquez |
|---|---|---|
| Price from | — | — |
| Free plan | — | — |
| Free trial | Yes | — |
| Open source | Yes | — |
| Web | — | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Active Metadata Management Software | 8.3 (#6) | 7.2 (#15) |
| Audience | Datastrato says its platform is for enterprise teams and developers working with metadata management, governance, and AI-ready infrastructure. | Marquez is designed for data teams managing metadata and lineage across data processing applications and pipelines. |
| Deployment | Datastrato Enterprise is installed from a Helm chart into a Kubernetes cluster and activated with a license key; local evaluation uses local Kubernetes. | The deployment guide describes Docker and Kubernetes deployments and says the HTTP API server uses PostgreSQL to store dataset, job, and run metadata. |
| Integrations | The product page names Spark, Flink, Trino, and Dremio as Iceberg catalog clients, and says the same namespace also resolves across BigQuery. | The site names Apache Airflow, Apache Spark, Apache Flink, dbt, and Dagster as integrations supported through the OpenLineage community. |
| Purpose | Datastrato is a federated metadata catalog built on Apache Gravitino and Iceberg REST Catalog for connecting data sources and governing access across clients. | Marquez consumes, stores, and visualizes OpenLineage metadata for use cases including data governance, data quality monitoring, and performance analytics. |
| Support | 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. | The site directs users to its Slack channel for help and feedback and encourages newcomers to join monthly community meetings. |
Plans
Datastrato Iceberg Lakehouse Platform
- Datastrato EnterprisePriced on request
In their own words
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.