Python Set Operations Explained: From Theory to Real-Time Applications
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AWS (Amazon Web Services) and PySpark are separate technologies, but they can be used together for certain purposes. Let me provide you with a beginner's guide for both AWS and PySpark separately.
Amazon Web Services (AWS) is a cloud computing platform that offers a wide range of services for computing power, storage, databases, machine learning, analytics, and more.
Go to the AWS homepage.
Click on "Create an AWS Account" and follow the instructions.
Install the AWS Command Line Interface (AWS CLI) on your local machine. Configure it with your AWS credentials using AWS configure.
AWS provides a variety of services. Familiarize yourself with core services like EC2 (Elastic Compute Cloud), S3 (Simple Storage Service), and IAM (Identity and Access Management).
PySpark is the Python API for Apache Spark, a fast and general-purpose cluster computing system. It allows you to write Spark applications using Python.
pip install pyspark
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("example").getOrCreate()
# Read from a CSV file
df = spark.read.csv("s3://your-s3-bucket/your-file.csv", header=True, inferSchema=True)
# Show the first few rows of the DataFrame
df.show()
# Perform transformations
df_transformed = df.select("column1", "column2").filter(df["column3"] > 10)
# Perform actions
result = df_transformed.collect()
# Write to Parquet format
df_transformed.write.parquet("s3://your-s3-bucket/output/parquet_data")
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