Strata offers a governed semantic layer that sits atop existing data warehouses, allowing analysts to create SQL‑free queries with automatic data blending and declarative metric definitions such as point‑in‑time snapshots and cohort analysis. The platform executes these queries on high‑performance compute engines to deliver sub‑second results on billions of rows and supports one‑click export to Excel or Google Sheets while enforcing role‑based access controls and auditability.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprise analysts spend days waiting for dashboards or data‑team tickets to answer simple ad‑hoc questions, and exporting data into Excel or Google Sheets often hits limits or requires manual SQL work. This bottleneck hampers rapid insight generation and forces non‑technical users into rigid reporting tools.
Solution
Strata delivers a governed semantic layer that sits atop the existing data warehouse, allowing non‑technical domain experts to formulate queries without writing SQL. The layer automatically understands data relationships, blends tables on‑the‑fly, and supports complex metric definitions such as point‑in‑time snapshots, multi‑step funnels, and cohort retention. Queries are executed by stacked high‑performance compute engines, providing sub‑second response times even on datasets containing billions of rows. Results can be exported to Excel or Google Sheets with a single click, preserving formatting and eliminating export caps. AI‑assisted YAML modeling lets data engineers define semantic models in their preferred Git‑based workflow, while a deterministic semantic API enables safe LLM‑driven data access. The platform enforces governance policies, ensuring data security and auditability while empowering analysts to explore the full warehouse freely.
Target Audience
The primary customers are business analysts and domain experts in mid‑size to large enterprises who need fast, self‑service access to the data warehouse, as well as data engineering teams that manage semantic models and governance policies.
Features
- Automatic data blending that resolves joins based on inferred relationships, removing the need for manual SQL joins.
- Advanced metric types (point‑in‑time snapshots, multi‑step funnels, cohort analysis) defined via declarative YAML configurations.
- Stacked compute engines optimized for low‑latency, high‑throughput query execution on massive data volumes.
- One‑click export to Excel and Google Sheets, delivering clean, ready‑to‑use datasets without export limits.
- AI‑powered semantic modeling with Git integration, enabling version‑controlled model development.
- Governed semantic API for LLMs and external applications, providing deterministic, permission‑aware query access.
- Role‑based access controls and audit logs to maintain compliance and data security.
- Scalability to support hundreds of concurrent users querying billions of rows without performance degradation.