Real-Time Analytics
Sub-second dashboards and data products on petabytes of data at any concurrency
Data Warehousing
Sub-second analytics on open lakehouse formats with no vendor lock-in
Observability in the AI Era
The most cost-effective alternative to Elasticsearch observability
Context Engineering
Hybrid search and fresh context for RAG, agents, and LLMs

Datastrato builds the open metadata layer for the modern data stack. Paired with VeloDB, you get a single catalog that spans warehouse tables, lakehouse files, and streaming sources — discoverable from BI tools, AI agents, and engines alike.
Datastrato unifies metadata across heterogeneous storage and engines, so teams can find, govern, and query data without managing a catalog per system. Schemas, lineage, and access policies stay consistent end-to-end.
Register VeloDB tables alongside Iceberg, Hive, and other sources in one catalog — no duplicate definitions to keep in sync.
Engines that speak the catalog can discover VeloDB tables and route queries appropriately, without bespoke connectors.
Apply access policies and capture lineage centrally; VeloDB inherits the same controls used across the rest of the stack.
Datastrato is built on open standards, so you keep portability and avoid lock-in to any single warehouse or platform.
Help analysts and AI agents find the right VeloDB table among thousands of datasets across systems.
Let federated query engines pick up VeloDB tables from the catalog and join them with lakehouse data on demand.
Manage row/column policies, tags, and lineage from one place; enforce them whether the query lands in VeloDB or elsewhere.
Tell us about your stack and use case. Our team will help you stand up VeloDB with Datastrato for your environment.