Let’s continue our ad hoc data analysis journey with the next tool: Apache Spark and in particular PySpark. In the previous post we used Linux command-line tools to perform a data analysis, which is a hard way for people who do not spend most of their time in terminal. PySpark should be much easier to understand for people who use SQL and Python for data analysis. We will use the same questions as previously about the number of streams per day/month, the number of games per day/month, most popular games and genres. In our setup we will use a Docker container provided by Jupyter (called pyspark-notebook) and run Spark in local mode (and write code in Jupyter notebook).
Posts with the tag Jupyter: