LakeQL
Introduction
  • Overview
  • Why LakeQL
  • Key Concepts
Getting Started
  • Prerequisites
  • Quickstart
  • Environment
  • First Run
Architecture
  • System Overview
  • Data Flow
  • Request Lifecycle
Configuration
  • Config File
  • Runtime Lifecycle
  • Environment Variables
create-app
  • Usage
  • Template Structure
  • Post-Creation
Guides
  • Custom Resolvers
  • Extending the Schema
  • Mutations
  • Set Up a Trino Mutation
  • Deploying
Endpoints
  • Definition Format
  • Field Types
  • Nested Fields
  • Mutations
  • Glossary
Contributing
  • Local Development
  • Contribution Guide
  • Architecture
GitHub
LakeQL
  1. LakeQL

LakeQL

Universal data access layer for modern platforms. Overview, setup, and guides for LakeQL.

LakeQL is a universal data access layer for modern platforms. It generates a type-safe GraphQL API across your data stack, served through a GraphQL server you compose from explicit building blocks. Point it at a data source, generate typed endpoints from your real tables, and query them. Adapters for Trino and Microsoft Fabric are available today. ClickHouse and DuckDB are on the roadmap.

New here? Read the introduction for the core concepts. Then follow the getting started guide to scaffold a project and make your first request.

Introduction

What LakeQL is, the problems it solves, and how its packages fit together.

Getting Started

Scaffold a project, configure it, and make your first request.

Architecture

How LakeQL is layered, how data flows, and the lifecycle of a request.

Configuration

The config file, the runtime lifecycle, and environment variables.

create-app

Scaffold a new LakeQL project with create-app.

Guides

Task-focused guides for common LakeQL workflows.

Endpoints

How LakeQL endpoints are generated, from a live table or from a definition file.

Glossary

Plain-language definitions of the lakehouse and LakeQL terms the rest of the documentation assumes.

Contributing

Set up the monorepo for local development and learn how to contribute.