Processing text data in Apache Spark

In this recipe, we will walk you through the step-by-step process of leveraging the power of Spark to handle and manipulate textual information efficiently. This recipe will equip you with the essential knowledge and practical skills needed to tackle text-based challenges using Apache Spark’s distributed computing capabilities.

How to do it…

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

   from pyspark.sql import SparkSession

   from pyspark.sql.functions import *

   spark = (SparkSession.builder

       .appName("text-processing")

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

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

       .getOrCreate())

   spark.sparkContext.setLogLevel("ERROR")

2. Load the data: We use the spark.read.format("csv") method to load the CSV data into a Spark...