Feature standard datasets

[ghanse]

Proposed changes

Added a few standard datasets:

• basic/geometries - WKT geometries of type POINT, LINESTRING, or POLYGON
• basic/process_historian - Time-series sensor data simulating values from a process historian system
• basic/telematics - Time-series location data simulating vehicle telematics
• benchmark/groupby - Benchmarking data with a set of grouping keys

Types of changes

What types of changes does your code introduce to dbldatagen?
Put an x in the boxes that apply

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Change to tutorials, tests or examples
• Non code change (readme, images or other non-code assets)
• Documentation Update (if none of the other choices apply)

Checklist

Put an x in the boxes that apply. You can also fill these out after creating the PR.
If you're unsure about any of them, don't hesitate to ask. We're here to help!
This is simply a reminder of what we are going to look for before merging your code.

• Lint and unit tests pass locally with my changes
• I have added tests that prove my fix is effective or that my feature works
• I have added necessary documentation (if appropriate)
• Any dependent changes have been merged and published in downstream modules
• Submission does not reduce code coverage numbers
• Submission does not increase alerts or messages from prospector / lint

Further comments

I have added several standard datasets along with unit tests where appropriate.

[CLAassistant]

All committers have signed the CLA.

[ronanstokes-db]

I'll review this and provide feedback after the initial merge of the feature

[ronanstokes-db]

Some general feedback:

1 - Can you add your tests to a separate test files - for example test_datasets_basic_geometries.py, test_datasets_historian.py etc so as to avoid all of the tests being in a single file with multiple contributors

2 - Separate out the groupby dataset into a separate PR - we've something similar in progress - so lets see if that addresses the same (or we can merge ideas)