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Explanation

PySpark is the Python API for Apache Spark. It is used for distributed data processing, ETL pipelines, big data analytics, and machine learning.

Code:

Explanation

Java applications can integrate with Spark using the Spark Java API to process large datasets across multiple machines.

Code:

SparkSession spark = SparkSession.builder().
appName("MyApp").master("local[*]").
getOrCreate();

Explanation

A DataFrame is Spark's primary data structure for working with structured data.

Code:

Dataset<Row> df = spark.read().
option("header", true).csv("employees.csv");

Explanation

Spark can filter and transform data efficiently using distributed processing.

Code:

Dataset<Row> highSalary = 
df.filter("salary > 50000");

Explanation

Spark DataFrames can be converted to JSON format for API responses or data exchange.

Code:

List<String> jsonData = df.toJSON().
collectAsList();