A local database service for converting directories of arbitrary files into validated assets and derived JSON metadata.

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License: MIT License (MIT)

Author: Alex Braun

Tags asset, project, data, json, database, datastore, flask, service

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Project description

Hidebound

A local database service for converting directories of arbitrary files into validated assets and derived metadata for export to databases like AWS S3 and MongoDB.

See documentation for details.

Installation

pip install hidebound

For Developers

Installation

  1. Install docker
  2. Install docker-machine (if running on macOS or Windows)
  3. Ensure docker-machine has at least 4 GB of memory allocated to it.
  4. cd hidebound
  5. chmod +x bin/hidebound
  6. bin/hidebound start

The service should take a few minutes to start up.

Run bin/hidebound --help for more help on the command line tool.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for the-new-flesh from gravatar.com the-new-flesh

Unverified details

These details have not been verified by PyPI
Project links
GitHub Statistics

View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

Meta

License: MIT License (MIT)

Author: Alex Braun

Tags asset, project, data, json, database, datastore, flask, service

Classifiers

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0.34.0

0.33.5

0.33.4

0.33.3

0.33.2

0.33.1

0.33.0

0.32.4

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0.31.5

0.31.4

0.31.1

0.30.0

0.29.0

0.26.1

0.25.1

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0.24.0

0.23.3

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0.20.1

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0.8.5

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This version

0.6.4

0.4.2

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0.2.3

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hidebound-0.6.4.tar.gz (27.8 kB view hashes)

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