Plan

Turn Github into a DataHub. Easy, fast, reliable data publishing.

Taglines

• Publish, organize and find data
• Wrangle, Preview, Deploy

More complex version with multiple documents, multiple assets

Workstreams

• Showcase: data and content presentation
• Publish: creating data-driven projects from github or via upload.
• Data API: an API to the data. Essential for larger datasets.
• Hub: teams, catalog and more.
• Comms: docs, community evangelism, blog etc

Milestones

• v1: "Publishing": Publish github README + csv to a showcase with a table and graph
  • Publish UI working
  • Updates on each push to git
  • Dashboard with reporting
  • Showcase is good
    • Table
    • Graph
  • Tried it out
    • working for 2+ core datasets
    • identified issues with our approach e.g. core datasets that don't preview
• v2: Data API: create a data api for a dataset
  • Larger data on github or git lfs
  • On push get a data API

v1 Sub-steps

(not necessarily ordered)

• Design
  • validated cloudflare as platform (e.g. can use for API)
  • define basic domain model
  • How to do dev deployment on cloudflare
• Chore
  • test framework
  • dev environment
  • set up changesets (?)
• Showcase. NB: at start do not even need to run off github or even our backend (can have Project stored as fixture or similar)
  • README only (MDX support)
  • README + inline data (csv or json)
  • README + external csv
  • …
• Publishing
  • "Manual" script creating a project
  • API for creating from github
  • Publish UI with github hook
• ? Search

Roadmap

There are many ways to schedule features into a roadmap. Furthermore, the specific sequence will evolve as we implement. Thus, rather than set out a detailed sequence we provide a backlog of major features by area.

Publishing

See design-publish-ui

• Connect github and go.
  • Create Project API: create, update etc
  • Create Project UI
• Publishing large datasets beyond size for git e.g. use git lfs or using your own storage etc
• Publish without github
  • "paste your data" (store your data and content)
  • From command line e.g. data deploy – see original ideas in ../docs/dms/datahub/v3

• Preview on command line

Showcase

See design-showcase

• Presentation
  • Metadata summary/overview
  • Markdown driven docs for your data (ie, README.md)
  • Data Explorer
  • Data Views like charts
• Document sources
  • README only (no data)
  • README with data resource or data package inline as yaml
  • datapackage.json + README
• Data (pre)views
  • xlsx
  • pdf
  • images
  • txt
  • json
  • xml
  • geo(json) etc.

Related:

• Access control: control who can access your data and share with team mates

Hub

• Search / Browse: metadata of all deployed datasets are available for search in the main portal (datahub.io/search)
  • Search page v0.1
• Dashboard
  • Dashboard v0.1: basic dashboard with user profile information and list of portals and their deployments
  • v0.2: usage information (space used etc)
• Plans and Billing
• Org/User page i.e. /@owner

Data APIs

Dedicated page: design-data-api

• Data APIs: Get powerful data APIs automatically
  • Custom APIs (see design thinking done on this in /docs/dms/data-api)
  • Metrics and user rate limiting (+ pay per use)

Workflows

• Workflows/Actions e.g. on every deploy do data validation, data summarization
  • Data validation
  • Data summary
  • Custom work flows

Comms

• Landing page

Backlog

• Import/Export: export to google sheets etc
• Preview for local datasets prior to deployment. Imagined in datahub-v3-2021
• Custom catalogs: Creating and deploying a catalog of multiple specific datasets e.g. I want to curate a set of climate datasets etc