Datastrato Iceberg Lakehouse Platform vs LavaLake
Datastrato Iceberg Lakehouse Platform
datastrato.aiLavaLake
lavalake.ioSide by side
| Figure | Datastrato Iceberg Lakehouse Platform | LavaLake |
|---|---|---|
| Price from | — | €249/mo |
| Free plan | — | Yes |
| Free trial | Yes | Yes |
| Open source | Yes | — |
| Web | — | Yes |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 4 |
| Spec score · Data Lakehouse Platforms | 8.4 (#4) | 7.9 (#8) |
| 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 reference deployment is a Docker Compose stack on one machine, with Ansible installation for Debian/Ubuntu; the Kubernetes Helm path has operational gaps including policy integration. |
| Founded | LinkedIn lists Datastrato's founding year as 2023. | 2026 |
| Purpose | Datastrato is a federated metadata catalog built on Apache Gravitino and Iceberg REST Catalog for connecting data sources and governing access across clients. | LavaLake combines a data warehouse and lakehouse with AI agents, SQL, and dashboards for deployment in a customer's own data center. |
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.
LavaLake
LavaLake combines a data warehouse and lakehouse with SQL, dashboards and AI agents for deployment in a customer’s own data center. Its assistant can find data, query metrics and create dashboards using the signed-in user’s permissions. An MCP server lets external agents use platform tools under those permissions. Read-only database sources include PostgreSQL, MySQL, MS SQL Server, SAP Business One and SAP S/4HANA. CSV and DATEV files can be imported, while PDF, Word and text documents can be searched. Hybrid search combines vector and keyword results using a locally run nomic-embed-text model with PostgreSQL and pgvector. The lakehouse uses Apache Iceberg on S3-compatible storage, Nessie as its versioned catalog and Trino as its query engine. Tableau and Power BI connect through a read-only HTTP feed that applies analyst identity and records data fetches in an audit chain. The maker says lakehouse data and local search stay on the customer’s network, while external AI providers receive conversation content and assistant tool results. Core is free with no time limit; paid plans start at €249/mo.