Databricks Notebooks vs Datastrato Iceberg Lakehouse Platform
Databricks Notebooks
databricks.com
Datastrato Iceberg Lakehouse Platform
datastrato.aiSide by side
| Figure | Databricks Notebooks | Datastrato Iceberg Lakehouse Platform |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | Yes | Yes |
| Open source | — | Yes |
| Web | Yes | — |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| Android | Yes | — |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 11 | 1 |
| Spec score · Data Lakehouse Platforms | 9.2 (#1) | 8.4 (#4) |
| Founded | 2013 | LinkedIn lists Datastrato's founding year as 2023. |
| Integrations | Databricks offers native cloud integrations for AWS, Azure and Google Cloud, including marketplace or console deployment paths. | The product page names Spark, Flink, Trino, and Dremio as Iceberg catalog clients, and says the same namespace also resolves across BigQuery. |
| What it does | Databricks notebooks are web-based code editors for interactive data analysis and viewing results. | Datastrato provides a federated metadata catalog and Iceberg REST platform for governing data and AI assets across clouds, engines and formats. |
Plans
Databricks Notebooks
- Free EditionFree
- Pay as you goPrice not published
- Pay as you goPriced on request
- Committed Use ContractsPrice not published
- Committed Use ContractsPriced on request
- Free EditionFree
- Free trialFree
- Artificial Intelligence$0.07 a month
- Data Engineering$0.15 a month
- Data Warehousing$0.22 a month
Datastrato Iceberg Lakehouse Platform
- Datastrato EnterprisePriced on request
In their own words
Databricks Notebooks
Databricks Notebooks has no write-up on Specifiction yet; its figures are above.
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.