Databricks Fundamentals for Beginners: What Actually Happens When You Execute a Spark DataFrame? Coming from SQL Server, understanding how Spark handles data is an important first step in learning Databricks. Consider this simple PySpark example. . 1. Read a Parquet dataset df = spark.read.parquet("/sales/sales.parquet") This creates a Spark DataFrame representing the dataset. Spark does not normally load every row into memory when this statement runs. 2. Apply a transformation filtered_df = df.filter("Region = 'Kerala'") This defines a filter operation. Spark generally builds a logical execution plan rather than immediately processing and storing the filtered rows. 3. Trigger execution filtered_df.show() "show()" is an action. It triggers execution of the required computation, reads the necessary data, applies the filter, and displays a limited number of rows. Does "show()" store the...