13 Project: Finding the fastest way to get around NYC
This chapter covers
- Setting up an Airflow pipeline from scratch
- Structuring intermediate output data
- Developing idempotent tasks
- Implementing one operator to handle multiple similar transformations
Transportation in New York City (NYC) can be hectic. It’s always rush hour, but luckily there are more alternative ways of transportation than ever. In May 2013, Citi Bike started operating in New York City with 6,000 bikes. Over the years, Citi Bike has grown and expanded and has become a popular method of transportation in the city.
Another iconic method of transportation is the Yellow Cab taxi. Taxis were introduced in NYC in the late 1890s and have always been popular. However, in recent years the number of taxi drivers has plummeted, and many drivers started driving for ride-sharing services such as Uber and Lyft.
Regardless of what type of transportation you choose in NYC, typically the goal is to go from point A to point B as fast as possible. Luckily the city of New York is very active in publishing data, including rides from Citi Bikes and Yellow Taxis.
Here, we try to answer this question: “If I were to go from A to B in NYC right now, which method of transportation is fastest?” We’ve created an Airflow mini project to extract and load data, transform it into a usable format, and ask the data which method of transportation is faster, depending on the neighborhoods you’re traveling between and the time of the day. [1]