March Madness + Data
Here at Databricks we like to use (you guessed it) data in our daily lives. Today kicks off a series called Databrags ๐ Databrags are glimpses into how Bricksters and community folks like you use data to solve everyday problems, excel in their personal lives, or simply just be really cool.
Knowledge is power and March is the month where basketball + powerful insights = ๐ค ๐ฐ
Our own @LiamClifford has created a notebook for us regarding the beloved tradition of March Madness ๐ ! Check it out to see a breakdown and a quick ETL example that outlines how YOU can use Databricksโ platform capabilities to help inform any last minute March Madness bracket decisions.
Link to DBC archive
Link to Source Python file
(easy โimport from fileโ optionsโ for you^)
In this demo-notebook Liam covers how to:
1.Leverage python's beautiful soup library to web-scrape college basketball game log information
2.Use apache spark and delta to transform our raw data into a cleaner tabular format (enabling further analysis)
Liam and I canโt wait to hear how this impacts your final bracket choices! Feel free to share your March Madness bracket below, or ask any questions, chat about insights, etc.!
Let the data-madness begin!
