{"categories":[{"label":"Database","url":"https://skillfed.io/packages/category/database/2"}],"enrichment":{"capability":"Intake provides a declarative data catalog system for describing, discovering, and loading datasets from multiple sources and formats, with support for remote storage and compute platforms.","skillfed_tags":["data-catalog","declarative-config","remote-storage"],"use_cases":["Define a shared data catalog in YAML so team members load datasets by name instead of hardcoding paths or credentials.","Discover and load datasets from a central repository without knowing their exact format or storage location.","Build reproducible data pipelines by versioning and referencing datasets through a catalog rather than file paths.","Integrate data from multiple remote sources (cloud storage, APIs, databases) under a unified interface.","Automate data loading and transformation workflows for machine learning or scientific computing projects."],"what_it_does":"Intake is a data access abstraction layer that lets you describe datasets declaratively\u2014via YAML or Python\u2014and organize them into searchable catalogs. Instead of scattering data-loading logic throughout your code, you define data sources once and reference them by name. It handles the mechanics of connecting to remote storage (S3, GCS, etc.), parsing different formats, and optionally transforming data on load.\n\nThe package is built on fsspec for filesystem abstraction, pyyaml for configuration, networkx for dependency graphs, and platformdirs for configuration storage. It's designed for teams sharing datasets and for workflows where reproducibility and data provenance matter. You can search catalogs, load data into memory or stream it, and chain transformations\u2014all without writing custom I/O code for each data source.","worth_installing":"Yes, if you work with multiple data sources or share datasets across a team. Intake reduces boilerplate and centralizes data access logic. However, note the active security advisory (GHSA-37g4-qqqv-7m99) and verify it does not affect your use case before deploying to production. The plugin ecosystem may require additional dependencies for your specific data sources."},"id":"intake","links":{"html":"https://skillfed.io/packages/intake","md":"https://skillfed.io/packages/intake.md","pypi":"https://pypi.org/project/intake/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-09","license_spdx":null,"license_treatment":"permissive","name":"intake","python_support":"supports_current","summary":"Data catalog, search and load"},"popularity":{"monthly_downloads":1351173,"position":4016,"tier":"top_5000"},"security":{"n_vulnerabilities":1},"version":"2.0.9"}