Sidemantic Documentation
Define metrics once, query them anywhere.
Getting Started
Core Concepts
Configuration
YAML file structure, database connections, models, dimensions, metrics, and CLI configuration for Sidemantic
Database Connections
Connect Sidemantic to DuckDB, MotherDuck, PostgreSQL, BigQuery, Snowflake, ClickHouse, Databricks, and Spark SQL with flexible connection string formats
Models
Define your data sources with models. Learn about dimensions, metrics, relationships, and time granularity.
Metrics
Define aggregations and calculations. Model-level metrics aggregate data, graph-level metrics combine them with formulas and automatic dependency detection.
Relationships
Define relationships between models for automatic joining.
Pre-Aggregation Recommendations
Automatically analyze query history and get optimal pre-aggregation recommendations with benefit scores.
Pre-Aggregations
Materialized rollup tables that store pre-computed aggregations for significant query performance improvements with automatic query routing and refresh strategies.
Query
Query the semantic layer using SQL syntax or the Python API
CLI
Command Line Interface
Tools for working with semantic layers from the terminal
Migrator
Migrate existing SQL queries to semantic layer by analyzing queries and generating model definitions.
Workbench
Interactive terminal UI for exploring semantic layers, writing SQL queries, and visualizing results
MCP Server
Enable AI assistants like Claude to query your semantic layer using the Model Context Protocol server