Big Data, No Big Deal: PySpark
summary
Demonstrates using PySpark to turn a single Washington Post police shootings CSV into a distributed dataset by writing it partitioned by state in parquet so Spark can run queries across many files, then creating a PySpark DataFrame that executes distributed selects, filters, group by and SQL and returns aggregated results from each partition; notes demo files remain local but scale on cloud, and shows Koalas adds pandas methods like value_counts to bridge gaps with pandas.
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