Data Whisperer
Bridge the gap between raw datasets and executive decisions with conversational SQL intelligence.
Cube (formerly Cube.js) is the market-leading semantic layer designed to sit between your data sources and downstream applications. By 2026, Cube has evolved into the critical infrastructure for 'Headless BI,' enabling teams to define their data models and business logic in a centralized location that serves every consumer: from internal BI tools like Tableau to custom-built SaaS dashboards and LLM-powered agents. The architecture is built on four core pillars: Semantic Modeling (defining metrics in YAML/JS), Security (centralized row-level access control), Caching (utilizing pre-aggregations for sub-second performance), and a Multi-protocol API (SQL, REST, and GraphQL). This decoupling of the data model from the visualization layer eliminates metric drift, ensuring that a single metric—like Monthly Recurring Revenue—is calculated identically across all platforms. In the era of AI, Cube serves as the essential 'Context Layer' for LLMs, providing a structured, governed interface for natural language queries against complex data warehouses like Snowflake, BigQuery, and Databricks.
Simultaneous support for REST, GraphQL, and the Postgres-compliant SQL API.
Bridge the gap between raw datasets and executive decisions with conversational SQL intelligence.
The semantic bridge between natural language intent and complex enterprise data silos.
The Multi-Agent Orchestration Fabric for Real-Time Cognitive Data Interoperability.
The AI-native semantic layer for translating natural language into high-performance SQL.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A purpose-built caching layer that stores aggregated data in Parquet format on S3/GCS.
Ability to generate schema dynamically based on user-provided metadata or runtime variables.
Structured metadata export designed specifically for LLM context injection (RAG).
JWT-based security layer that implements row-level and column-level security at the API level.
Automatically routes queries to different physical databases based on the user's security token.
Native integration with GitHub/GitLab for version-controlled data modeling.
Providing customers with high-performance, interactive dashboards without managing complex SQL logic in the frontend.
Registry Updated:2/7/2026
AI hallucinations when trying to generate SQL for complex metrics like 'Net Retention Rate'.
Moving from Looker or old BI stacks while maintaining a consistent metric layer for all new tools.