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Code Sample

from pyspark import SparkContext

# Create a SparkContext object
sc = SparkContext("local", "Hello World Example")

# Create an RDD (Resilient Distributed Dataset) with the "Hello World" message
hello_world_rdd = sc.parallelize(["Hello World"])

# Use the `count` action to trigger the computation and print the result
print("Number of elements in the RDD:", hello_world_rdd.count())


# Stop the SparkContext
sc.stop()

// hello world function
              from pyspark import SparkContext

              # Create a SparkContext object
              sc = SparkContext("local", "Hello World Example")
              
              # Create an RDD (Resilient Distributed Dataset) with the "Hello World" message
              hello_world_rdd = sc.parallelize(["Hello World"])
              
              # Use the `count` action to trigger the computation and print the result
              print("Number of elements in the RDD:", hello_world_rdd.count())
              
              # Stop the SparkContext
              sc.stop()

Python Example:

#!/bin/bash

# Specify the Git repository URL and the directory to clone
repo_url="https://github.com/user/repo.git"
clone_dir="repo"

# Clone the Git repository
git clone $repo_url $clone_dir

# Go to the clone directory
cd $clone_dir

// hello world function
            from pyspark import SparkContext

            # Create a SparkContext object
            sc = SparkContext("local", "Hello World Example")
            
            # Create an RDD (Resilient Distributed Dataset) with the "Hello World" message
            hello_world_rdd = sc.parallelize(["Hello World"])
            
            # Use the `count` action to trigger the computation and print the result
            print("Number of elements in the RDD:", hello_world_rdd.count())
            
            # Stop the SparkContext
            sc.stop()

Python

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AWS

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AWS Example:

import boto3

# Connect to the DynamoDB service
 dynamodb = boto3.client("dynamodb")

# Specify the DynamoDB table and the item to put
table_name = "my-table"
item = {     
 "key": {"S": "value1"},
 "sort_key": {"N": "1"},
 "attribute": {"S": "value2"}
 }
 
# Put the item in the DynamoDB table
dynamodb.put_item(TableName=table_name, Item=item)

// hello world function
            from pyspark import SparkContext

            # Create a SparkContext object
            sc = SparkContext("local", "Hello World Example")
            
            # Create an RDD (Resilient Distributed Dataset) with the "Hello World" message
            hello_world_rdd = sc.parallelize(["Hello World"])
            
            # Use the `count` action to trigger the computation and print the result
            print("Number of elements in the RDD:", hello_world_rdd.count())
            
            # Stop the SparkContext
            sc.stop()

DevOps Example::

PHP

// hello world function
function hell_world( $post_type_params ) {             
$post_type_params['hierarchical'] = true;
if ( empty($post_type_params) ) {
   helper();
}
return 'success';
}

// hello world function
function hell_world( $post_type_params ) {             
$post_type_params['hierarchical'] = true;
if ( empty($post_type_params) ) {
   helper();
}
return 'success';
}

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DevOps Example::

DevOps

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Python

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