A DataFrame in Spark is a distributed collection of rows and columns. It is the same as a table in a relational database or an Excel sheet. A Spark RDD/DataFrame is efficient at processing large amounts of data and has the ability to handle petabytes of data, whether structured or unstructured.
Spark optimizes queries on data by organizing the DataFrame into columns, which helps Spark understand the schema. Some of the most frequently used SQL operations include subsetting the data, merging the data, filtering, selecting specific columns, dropping columns, dropping all null values, and adding new columns, among others.
For summary statistics of data, we can use the spark_df.describe().show() function, which will provide information on count, mean, standard deviation, max, and min for all the columns in the DataFrame.
For example, in the dataset that we have considered—the bank marketing...
