Orionbelt: Open-source Semantic Layer for AI and Analytics

Orionbelt: Open-Source Semantic Layer for AI and Analytics

Orionbelt is an open-source semantic sidecar designed to bridge the gap between raw data and intelligent applications. It transforms your business logic, defined in YAML, into optimized SQL queries, semantic context, key performance indicators (KPIs), and data quality rules. This allows AI models and analytics tools to access and interpret data with a standardized, business-aware layer, ensuring consistency and accuracy across your data ecosystem.

What it Does

Orionbelt acts as an intermediary, interpreting your semantic models written in YAML. It then compiles these models into executable SQL that can be directly used by databases. This process generates a rich semantic context, defining how data should be understood and used. Furthermore, it allows for the definition and enforcement of KPIs, providing standardized metrics for performance tracking, and establishes data quality rules to maintain the integrity of your information. Essentially, it creates a unified semantic understanding of your data that is accessible to both AI agents and traditional analytics platforms.

Key Features

• YAML-based Semantic Modeling: Define your business logic, metrics, and data rules in an accessible YAML format.
• SQL Compilation: Automatically generates optimized SQL queries from your semantic models.
• Semantic Context Generation: Provides a consistent, business-aware interpretation of your data.
• KPI Definition and Management: Standardize and track key performance indicators across your organization.
• Data Quality Rule Enforcement: Integrate data quality checks directly into your semantic layer.
• Open-Source: Freely available and adaptable for your specific needs.
• MCP Type: Integrates seamlessly into the MCP ecosystem for AI development.

Who it's For

Orionbelt is built for developers and data professionals working with AI and analytics. This includes:

• AI Engineers: Who need a reliable and consistent way to feed structured, business-aware data into their AI models.
• Data Engineers: Looking to standardize data interpretation and ensure data quality across various data sources.
• Analytics Developers: Seeking to build robust and accurate dashboards and reports based on a defined semantic layer.
• Teams Building Data-Centric Applications: Requiring a unified understanding of business metrics and data relationships.