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version-3.5.x
version-3.5.x
  • Introduction
  • Installation
  • Quick start
  • Mental model
  • Architecture
  • Walkthrough
    • Editing a Package
    • Uploading a Package
    • Installing a Package
    • Getting Data from a Package
    • Working with the Catalog
    • Working with a Bucket
  • API Reference
    • quilt3
    • quilt3.Package
    • quilt3.Bucket
    • CLI, environment
    • Known limitations
  • Catalog
    • Configuration
    • Preview
    • Search & query
    • Metadata for teams
    • Admin UI
    • Embed
  • Advanced Usage
    • Filtering a Package
    • .quiltignore
    • Materialization
    • Working with Manifests
    • S3 Select
    • Workflows
    • Enterprise install
    • S3 Events, EventBridge
  • More
    • Frequently Asked Questions
    • Troubleshooting
    • Contributing
    • Changelog
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On this page
  • Quilt is a self-organizing data hub
  • Python Quick start, tutorials
  • Quilt in action
  • Who is Quilt for?
  • What does Quilt do?
  • How does Quilt work?
  • Use cases
  • Roadmap

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Introduction

NextInstallation

Last updated 3 years ago

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Quilt is a self-organizing data hub

Python Quick start, tutorials

If you have Python and an S3 bucket, you're ready to create versioned datasets with Quilt. Visit the for installation instructions, a quick start, and more.

Quilt in action

  • is a petabyte-scale open

    data portal that runs on Quilt

  • includes case studies, use cases, videos,

    and instructions on how to run a private Quilt instance

  • shows how to use Quilt for real world projects

Who is Quilt for?

Quilt is for data-driven teams and offers features for coders (data scientists, data engineers, developers) and business users alike.

What does Quilt do?

Quilt manages data like code so that teams in machine learning, biotech, and analytics can experiment faster, build smarter models, and recover from errors.

How does Quilt work?

Quilt consists of a Python client, web catalog, lambda functions—all of which are open source—plus a suite of backend services and Docker containers orchestrated by CloudFormation.

Use cases

  • Share data at scale. Quilt wraps AWS S3 to add simple URLs, web preview for large files, and sharing via email address (no need to create an IAM role).

  • Understand data better through inline documentation (Jupyter notebooks, markdown) and visualizations (Vega, Vega Lite)

  • Discover related data by indexing objects in ElasticSearch

  • Model data by providing a home for large data and models that don't fit in git, and by providing immutable versions for objects and data sets (a.k.a. "Quilt Packages")

  • Decide by broadening data access within the organization and supporting the documentation of decision processes through audit-able versioning and inline documentation

Roadmap

I - Performance and core services

II - CI/CD for data

III - Storage agnostic (support Azure, GCP buckets)

IV - Cloud agnostic

The backend services are available under a paid license on .

quiltdata.com
Quilt docs
open.quiltdata.com
quiltdata.com
Versioning data and models for rapid experimentation in machine learning
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chat on_slack
codecov
pypi