InnerSourceCommons / InnerSourceCommons/innersource-working-group

Add a section on making your data InnerSource-ready

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Dominant language
Ruby
Stars
26
Forks
4
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No merged PRs in 30d

Description

Working document: https://docs.google.com/document/d/1bodpI0Gz-vxd2nbxUOEgHlEJASCBqqT_urK3kieQ1_0/edit?usp=sharing

Data lakes, data warehouses, etc.

Patterns on making them discoverable in the most popular tooling.

How do we populate the data storage systems with our ISPO guidance to help people with all of this:

https://innersourcecommons.github.io/managing-innersource-projects/innersource-and-ai/shaping-for-ai.html

https://opendataproducts.org/

Are these patterns, or a page in the Managing InnerSource Projects book?

  • Customer Solutions
  • GitHub repositories
    • What fields are necessary?
    • How can we find the repo automatically using AI?
  • User data
  • Infrastructure Footprint (GCP, AWS, Azure, Etc.)
  • Software Development teams (Jira, GitHub Issues, AHA, Etc.)
  • Architectural/system diagrams
  • Design artifacts (Figma, Google's Stitch)
  • Artifact Repositories (Artifactory, GitHub Artifacts)
  1. Expose all these data products to Agents
  2. Automate InnerSource activities
  3. A working model between a data science organization in your company and the ISPO.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with the linked Google working document and the referenced InnerSource and Open Data Products guidance. Clarify whether this belongs as patterns or a page in the Managing InnerSource Projects book, then define coverage for the listed data sources and tooling; done means the scope and resulting guidance are agreed.

Written by the indexing model from the issue text.

Assessment

Tech stack
aws, azure, gcp, github
Domain
cloud, data-engineering, documentation
Issue type
Documentation
Difficulty
5/5
Estimated time
Over a week
Activity status
Quiet
Clarity
Needs clarification
Newbie friendliness
30/100

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