There's a zomato riders' dataset available on kaggle: https://www.kaggle.com/datasets/cbhavik/swiggyzomato-order-information
Based on that dataset create a tutorial to identify:
- the "wormholers": riders with the highest (perc80) delivery speed per mile and day
(- the "cheaters": riders with fast acceptance time (perc80) but slow delivery speed per mile)
The tutorial should cover:
Given: there's a kafka cluster already running (perhaps an tutorial of it's own?)
Given: the data is available at a topic (small linux commandline is fine)
- App Foundations: setup a micronaut Kipes app. (from(topic).logInfo(date, rider, distance))
- Calculating delivery speed per mile into new topic (automatic topic creation vs. SDK, incl. how to reset the event index for the consumer via cmd line)
- Adding perc80 (delivery speed per mile) per day
- Filter and Table per day
(The "cheater" addition could show how to use joins but that could get too complicated for one tutorial)
There's a zomato riders' dataset available on kaggle: https://www.kaggle.com/datasets/cbhavik/swiggyzomato-order-information
Based on that dataset create a tutorial to identify:
(- the "cheaters": riders with fast acceptance time (perc80) but slow delivery speed per mile)
The tutorial should cover:
Given: there's a kafka cluster already running (perhaps an tutorial of it's own?)
Given: the data is available at a topic (small linux commandline is fine)
(The "cheater" addition could show how to use joins but that could get too complicated for one tutorial)