Reading Parquet data with Apache Spark

Apache Parquet is a columnar storage format designed to handle large datasets. It is optimized for the efficient compression and encoding of complex data types. Apache Spark, on the other hand, is a fast and general-purpose cluster computing system that is designed for large-scale data processing.

In this recipe, we will explore how to read Parquet data with Apache Spark using Python.

How to do it...

1. Import libraries: Import the required libraries and create a SparkSession object:

   from pyspark.sql import SparkSession

   spark = (SparkSession.builder

       .appName("read-parquet-data")

       .master("spark://spark-master:7077")

       .config("spark.executor.memory", "512m")

       .getOrCreate())

   spark.sparkContext.setLogLevel("ERROR")

2. Load the Parquet data: We use the spark.read.format("parquet") method to...