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Introduction to Spark Scala302 days
RDD Basics and Transformations403 days
Spark SQL and DataFrames504 days
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Machine Learning with MLlib706 days
GraphX and Graph Processing807 days
Performance Optimization908 days
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Data Manipulation Using Spark Scala API

import org.apache.spark.sql.SparkSession object SparkScalaExample { def main(args: Array[String]): Unit = { // Create a SparkSession val spark = SparkSession.builder() .appName("Spark Scala Example") .master("local[*]") // Use all available cores locally .getOrCreate() // Read CSV file into a DataFrame val df = spark.read .option("header", "true") .csv("input.csv") // Perform a simple transformation (e.g., adding a new column) val transformedDF = df.withColumn("NewColumn", df("SomeColumn") + 1) // Write the transformed DataFrame to a new CSV file transformedDF.write .option("header", "true") .csv("output.csv") // Stop the SparkSession spark.stop() } }

In this example:

  • We create a SparkSession to initialize Spark functionality.
  • Read data from a CSV file (input.csv) into a DataFrame.
  • Perform a transformation by adding a new column (NewColumn).
  • Write the transformed DataFrame to another CSV file (output.csv).
  • Finally, stop the SparkSession to release resources.

This is a basic example to illustrate how Spark's Scala API can be used for data manipulation tasks. Depending on your specific requirements, you can extend this example with more complex transformations, data manipulations, or advanced Spark features.

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