CLI: Improve dataset list output

[billgeo]

User Story

So that I can easily find the dataset I am looking for, as a Data Maintainer, I want to sort the dataset list output.

Instead of the default AWS behaviour which is an unordererd list

Acceptance Criteria

• [ ] Given more than one dataset is present, when do a dataset list, then by default it is sorted oldest to newest created date
• [ ] Given more than one dataset is present, when I speficy a --sort, or similar, parameter, then I can specify how I want the list sorted

Additional context

Sorting option could include:

• oldest to newest
• newest to oldest
• alphabetical

Tasks

• [ ] ...
• [ ] ...

Definition of Ready

• [ ] This story is ready to work on
  • [ ] Independent (story is independent of all other tasks)
  • [ ] Negotiable (team can decide how to design and implement)
  • [ ] Valuable (from a user perspective)
  • [ ] Estimate value applied (agreed by team)
  • [ ] Small (so as to fit within an iteration)
  • [ ] Testable (in principle, even if there isn't a test for it yet)
  • [ ] Environments are ready to meet definition of done
  • [ ] Resources required to implement will be ready
  • [ ] Everyone understands and agrees with the tasks to complete the story
  • [ ] Release value (e.g. Iteration 3) applied
  • [ ] Sprint value (e.g. Aug 1 - Aug 15) applied

Definition of Done

• [ ] This story is done:
  • [ ] Acceptance criteria completed
  • [ ] Automated tests are passing
  • [ ] Code is peer reviewed and pushed to master
  • [ ] Deployed successfully to test environment
  • [ ] Checked against CODING guidelines
  • [ ] Relevant new tasks are added to backlog and communicated to the team
  • [ ] Important decisions recorded in the issue ticket
  • [ ] Readme/Changelog/Diagrams are updated
  • [ ] Product Owner has approved acceptance criteria as complete
  • [ ] Meets non-functional requirements:
    • [ ] Scalability (data): Can scale to 300TB of data and 100,000,000 files and ability to increase 10% every year
    • [ ] Scability (users): Can scale to 100 concurrent users
    • [ ] Cost: Data can be stored at < 0.5 NZD per GB per year
    • [ ] Performance: A large dataset (500 GB and 50,000 files - e.g. Akl aerial imagery) can be validated, imported and stored within 24 hours
    • [ ] Accessibility: Can be used from LINZ networks and the public internet
    • [ ] Availability: System available 24 hours a day and 7 days a week, this does not include maintenance windows < 4 hours and does not include operational support
    • [ ] Recoverability: RPO of fully imported datasets < 4 hours, RTO of a single 3 TB dataset < 12 hours