Finding the answers, we need isn’t always as easy as it would seem. Before analysis can begin, we need to combine data from multiple disparate locations, transform oddly formatted or semi-structured files, find a secure place to store the data, and make it available for analysis. Today’s session will show how you can accomplish all of that -and more- using Snowflake.
The “story” of this lab is based on the analytics team at Citi Bike, a real, citywide bike share system in New York City, USA. This team wants to be able to run analytics on data to better understand their riders and how to serve them best.
We will load structured .csv data from rider transactions into Snowflake. This comes from Citi Bike internal transactional systems. Then later we will load open-source, semi-structured JSON weather data into Snowflake to see if there is any correlation between the number of bike rides and weather.
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