Introduction

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 Quilt docs for installation instructions, a quick start, and more.

Quilt in action

• open.quiltdata.com is a petabyte-scale open data portal that runs on Quilt

• quiltdata.com includes case studies, use cases, videos, and instructions on how to run a private Quilt instance

• Versioning data and models for rapid experimentation in machine learning 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.

The backend services are available under a paid license on quiltdata.com.

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

• Address performance issues with push (e.g. re-hash)

• Provide Presto-DB-powered services for filtering package repos with SQL

• Transition S3 manifests to Apache Iceberg tables

II - CI/CD for data

• Ability to fork/merge packages

• Data quality monitoring

III - Storage agnostic (support Azure, GCP buckets)

• Evaluate min.io and ceph.io as shims

• Evaluate feasibility of on-prem local storage as a repo

IV - Cloud agnostic

• Evaluate K8s and Terraform to replace CloudFormation

• Shim lambdas (consider serverless.com)

• Shim ElasticSearch (consider SOLR)

• Shim IAM via RBAC