How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Quilt is a Scientific Data Management Platform on AWS that helps teams and AI find, trust, and reuse data through deeply versioned, context-rich data packages.
The main features of quiltdata/quilt are: Data Management, Experiment and Data Management.
Open-source alternatives to quiltdata/quilt include: treeverse/lakefs — lakeFS is a data lake versioning system that provides Git-like branching and commits for large datasets stored in… iterative/dvc — DVC is a data versioning tool and pipeline orchestrator designed to track large datasets and machine learning models.… dolthub/dolt — Dolt is a relational database engine that integrates version control directly into the database management layer. It… activeloopai/hub — Hub is a multimodal AI data lake and vector database designed for storing and querying embeddings, text, audio, and… aimhubio/aim — Aim is an open-source platform for logging, visualizing, and comparing machine learning training runs and LLM traces.… ahmed-ali/jsonexport — JSONExport is a multi-language code generator and JSON schema converter that transforms JSON data structures into…
DVC is a data versioning tool and pipeline orchestrator designed to track large datasets and machine learning models. It functions as a system for managing large data artifacts by storing lightweight metadata in version control while keeping the actual binaries in a separate cache. The project serves as an experiment tracker and remote storage synchronizer, enabling the execution and comparison of machine learning iterations based on hyperparameters and performance metrics. It provides a bridge for pushing and pulling these large data artifacts between local environments and cloud or on-premi
lakeFS is a data lake versioning system that provides Git-like branching and commits for large datasets stored in object storage. It functions as a version control layer, enabling the creation of immutable snapshots, atomic commits, and zero-copy branching to create isolated environments for data experimentation without duplicating physical files. The system serves as an S3-compatible storage gateway and an Iceberg REST catalog, allowing standard cloud storage protocols and compatible clients to manage versioned tables. It acts as a data quality gatekeeper by using an event-driven hook system
Dolt is a relational database engine that integrates version control directly into the database management layer. It functions as a version-controlled SQL database that tracks every row and schema change using a commit-based history, allowing users to branch, merge, and audit data modifications. By implementing a wire-protocol-compatible server, the system enables standard SQL clients and tools to interact with versioned data as if they were connecting to a traditional relational database. The platform distinguishes itself by applying repository-style workflows to data management, including s
Hub is a multimodal AI data lake and vector database designed for storing and querying embeddings, text, audio, and images. It functions as a dataset version control system and a machine learning data streaming engine to support large-scale model training. The system utilizes a serverless PostgreSQL vector store to index high-dimensional embeddings for semantic search. It provides a visual interface for inspecting multimodal datasets and viewing annotations such as bounding boxes and masks. The platform handles cloud-agnostic storage synchronization and implements lazy, compressed data strea