AI Summary
→ WHAT IT COVERS SurrealDB 3.0 combines relational, document, graph, time series, and vector databases into one multimodal system. Tobie Morgan Hitchcock explains event-driven AI applications, surrealism functions, and building data-centric workflows without complex infrastructure. → KEY INSIGHTS - **Multimodal querying:** SurrealDB stores data as documents but queries across key-value, tabular, time series, graph, and vector modalities simultaneously using SurrealQL, eliminating the need for three to four separate database systems and data duplication. - **Surrealism functions:** Developers write modular functions in Rust, JavaScript, or Python that execute directly alongside data in the database. Functions are versioned like Docker containers, enabling AB testing and rollback capabilities for AI-powered data processing workflows. - **Temporal querying:** SurrealDB enables time-travel queries to view entire datasets and graph relationships at any historical point. Combined with versioned functions, organizations can reproduce exact AI responses and data states for compliance and debugging purposes. - **Permissions model:** Security rules use SQL-like queries to define field-level and document-level access based on authenticated user attributes from OAuth, SAML, or custom systems, eliminating the need for separate permission layers in application code. → NOTABLE MOMENT Hitchcock reveals SurrealDB can replay any AI response ever generated by combining temporal queries that reconstruct historical data states with versioned functions, solving reproducibility requirements for regulated industries deploying large language models without full explainability. 💼 SPONSORS None detected 🏷️ Multimodal Databases, Event-Driven Architecture, AI Infrastructure, Database Security