Apache Spark
- Apache Spark: Big Data Processing & Analytics
- Spark DataFrames: Features, Use Cases & Optimization for Big Data
- Spark Architecture
- Dataframe create from file
- Dataframe Pyspark create from collections
- Spark Dataframe save as csv
- Dataframe save as parquet
- Dataframe show() between take() methods
- Apache SparkSession
- Understanding the RDD of Apache Spark
- Spark RDD creation from collection
- Different method to print data from rdd
- Practical use of unionByName method
- Creating Spark DataFrames: Methods & Examples
- Setup Spark in PyCharm
- Apache Spark all APIs
- Spark for the word count program
- Spark Accumulators
- aggregateByKey in Apache Spark
- Spark Broadcast with Examples
- Spark combineByKey
- Apache Spark Using countByKey
- Spark CrossJoin know all
- Optimizing Spark groupByKey: Usage, Best Practices, and Examples
- Mastering Spark Joins: Inner, Outer, Left, Right & Semi Joins Explained
- Apache Spark: Local Mode vs Cluster Mode - Key Differences & Examples
- Spark map vs flatMap: Key Differences with Examples
- Efficient Data Processing with Spark mapPartitionsWithIndex
- Spark reduceByKey with 5 Real-World Examples
- Spark Union vs UnionAll vs Union Available – Key Differences & Examples
RDD creation from collection
In the world of big data processing, Resilient Distributed Datasets (RDDs) stand tall as a fundamental data structure in Apache Spark. RDDs form the backbone of Spark's distributed computing prowess, offering fault tolerance and efficient data processing. In this article, we will dive deep into RDD creation from collection, exploring the different methods and best practices, along with real-world use cases and benefits.Understanding RDDs Before delving into RDD creation, let’s grasp the essence of RDDs. RDDs are immutable, fault-tolerant collections of objects distributed across multiple nodes in a cluster. They allow data to be processed in parallel, making them perfect for handling vast datasets.
# Importing SparkContext
from pyspark import SparkContext
# Create a SparkContext
sc = SparkContext("local", "RDDFromList")
# Sample data list
data_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# Creating an RDD from the list
rdd = sc.parallelize(data_list)