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Lance: 100x Faster Data Access

Faster data analysis for founders, compatible with Pandas and PyTorch, with 6.8k+ GitHub stars

From the repo · “Open Lakehouse Format for Multimodal AI. Convert from Parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..”

7,185 stars+32★ this week898 forksRustGuide quality 8/10 · reviewed Jul 13Updated 10/21/2026100% free · open source
What it does

Lance enables founders to accelerate data analysis by converting Parquet files to its Open Lakehouse Format, allowing for 100x faster random access, vector index, and data versioning.

When to use it
  • •When working with large datasets and needing faster data access
  • •When integrating with popular data analysis libraries like Pandas, PyTorch, or DuckDB
  • •When requiring data versioning and efficient random access for machine learning model training
Ready-to-paste prompt
Convert a sample Parquet file to Lance format and read it with Pandas: `lance convert -i sample.parquet -o sample.lance; import pandas as pd; df = pd.read_lance('sample.lance'); print(df.head())`
Heads up: Ensure you have Rust and Cargo installed on your system, as Lance is built with Rust and relies on the Cargo package manager for installation and dependency management.
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

✅ Light setup

Installs with a command or two; your AI agent can do it for you.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/lance && curl -fsSL https://workflowstacks.com/api/skills/lance/claude-skill -o ~/.claude/skills/lance/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How Lance: 100x Faster Data Access works
Codeflow
Free to inspect

Lance: 100x Faster Data Access is a very large Rust project (~632k lines across 1153 code files, plus 196 test files). Setup is light: installs like a normal app. Reading the code is optional. Last commit this month, Apache-2.0 license, has a test suite.

Size
Very large codebase
~632k lines · 1153 code files · days to read — use, don't read
Setup
One-command install
Installs like a normal app. Reading the code is optional.
Runs on
Rust · Docker
No API keys detected
Rust 79%HTML 7%Python 7%Java 5%Jupyter Notebook 1%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    AGENTS.md
    The instructions the AI actually follows
  3. 3
    Cargo.toml
    Dependencies and the commands it exposes
  4. 4
    skills/README.md
    Inside skills/ — an example of what the AI is told to do
What's in each folder
skills/Prompts, skills & agent definitions5 files
docs/Documentation146 files
rust/Folder888 files
java/Folder269 files
python/Folder245 files
test_data/Folder234 files
benchmarks/Evaluations & benchmarks46 files
ci/Folder17 files
READMEHas testsDocumentedCI checksDocker readyApache-2.0 licenseUpdated this month
Quick Actions
Details
Creator
lance-format
Language
Rust
Category
analytics
Published
7/7/2022

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Featured on WorkflowStacks
[![Featured on WorkflowStacks](https://workflowstacks.com/api/badge/lance.svg)](https://workflowstacks.com/skills/lance?utm_source=github&utm_medium=badge)
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