TrinityLake

An Open Lakehouse Format for Big Data Analytics, ML & AI

Introduction

TrinityLake is an Open Lakehouse Format for Big Data Analytics, ML & AI. It allows anyone to build a fully functional lakehouse with storage (e.g. S3, HDFS) as the only dependency.

The TrinityLake format defines different objects in a lakehouse and provides a consistent and efficient way for accessing and manipulating the interactions among these objects. It offers the following key features:

• Storage only as a lakehouse solution that works exactly the same way locally, on premise and in the cloud
• Multi-object multi-statement transactions with standard SQL BEGIN and COMMIT semantics
• Consistent time travel and snapshot export across all objects in the lakehouse
• Distributed transactions which can enable use cases like complicated write-audit-publish workflows
• Compatibility with open table formats like Apache Iceberg, supporting both standard SQL MANAGED and EXTERNAL as well as federation-based access patterns.
• Compatibility with open catalog standards like Apache Iceberg REST Catalog specification, serving as a highly scalable yet extremely lightweight backend implementation

For more details about the format, how to get started and how to join our community, please visit trinitylake.io.

Building

Java SDK

TrinityLake Java SDK is built using Gradle with Java 11, 17, 21, or 23.

• Build and run tests: ./gradlew build
• Build without running tests: ./gradlew build -x test -x integrationTest
• Fix code style and formatting: ./gradlew spotlessApply

Project Website

The TrinityLake project website is built using the mkdocs-material framework with a few other plugins.

First time setup

python3 -m venv env
source env/bin/activate
pip install mkdocs-material
pip install mkdocs-awesome-pages-plugin

Serve website

source env/bin/activate
mkdocs serve