A Spark DataFrame is a distributed collection of rows organized into named, typed columns, comparable to a table. It is immutable: each operation returns a new DataFrame. Spark optimizes the overall execution plan before distributing the computation across the cluster.
Example
df = spark.read.parquet('ventes.parquet').filter('montant > 100')