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+---
+language: Spark
+category: tool
+tool: Spark
+filename: learnspark-en.spark
+contributors:
+    - ["Scronge", "https://github.com/Scronge"]
+---
+
+[Spark](https://spark.apache.org/) is an open-source distributed data processing framework that enables large-scale data processing across clusters. This guide covers the basics of **Apache Spark** using PySpark, the Python API.
+
+```python
+# Setting Up Spark
+from pyspark.sql import SparkSession
+
+spark = SparkSession.builder \
+    .appName("RealWorldExampleApp") \
+    .getOrCreate()
+
+# Working with Larger DataFrames
+# Sample data for a retail dataset with multiple columns for complex queries
+data = [
+    ("Alice", "Electronics", 30, 200),
+    ("Bob", "Clothing", 40, 150),
+    ("Carol", "Electronics", 25, 300),
+    ("Dave", "Home Goods", 35, 100),
+    ("Eve", "Clothing", 28, 80),
+    ("Frank", "Home Goods", 40, 120)
+]
+columns = ["Name", "Category", "Age", "PurchaseAmount"]
+
+df = spark.createDataFrame(data, columns)
+df.show()
+# +-----+-----------+---+--------------+
+# | Name|   Category|Age|PurchaseAmount|
+# +-----+-----------+---+--------------+
+# |Alice|Electronics| 30|           200|
+# |  Bob|   Clothing| 40|           150|
+# |Carol|Electronics| 25|           300|
+# | Dave| Home Goods| 35|           100|
+# |  Eve|   Clothing| 28|            80|
+# |Frank| Home Goods| 40|           120|
+# +-----+-----------+---+--------------+
+
+# Transformations and Actions
+
+# Filtering data to select customers over 30 with purchases above $100
+filtered_df = df.filter((df.Age > 30) & (df.PurchaseAmount > 100))
+filtered_df.show()
+# +-----+-----------+---+--------------+
+# | Name|   Category|Age|PurchaseAmount|
+# +-----+-----------+---+--------------+
+# |  Bob|   Clothing| 40|           150|
+# |Frank| Home Goods| 40|           120|
+# +-----+-----------+---+--------------+
+
+# Grouping and Aggregations
+# Calculate total purchases by category
+category_totals = df.groupBy("Category").sum("PurchaseAmount")
+category_totals.show()
+# +-----------+------------------+
+# |   Category|sum(PurchaseAmount)|
+# +-----------+------------------+
+# |Electronics|               500|
+# |   Clothing|               230|
+# | Home Goods|               220|
+# +-----------+------------------+
+
+# SQL Queries
+
+# Registering DataFrame as a SQL temporary view
+df.createOrReplaceTempView("customers")
+high_spenders = spark.sql("SELECT Name, Category, PurchaseAmount FROM customers WHERE PurchaseAmount > 100")
+high_spenders.show()
+# +-----+-----------+--------------+
+# | Name|   Category|PurchaseAmount|
+# +-----+-----------+--------------+
+# |Alice|Electronics|           200|
+# |  Bob|   Clothing|           150|
+# |Carol|Electronics|           300|
+# |Frank| Home Goods|           120|
+# +-----+-----------+--------------+
+
+# Reading and Writing Files
+
+# Reading from CSV with additional options
+csv_df = spark.read.csv("path/to/large_retail_data.csv", header=True, inferSchema=True, sep=",")
+csv_df.show(5)  # Display only first 5 rows for preview
+
+# Writing DataFrame to Parquet format for optimized storage and access
+df.write.parquet("output/retail_data")
+
+# RDD Basics
+
+# Creating an RDD and performing complex transformations
+sales_data = [(1, 100), (2, 150), (3, 200), (4, 250)]
+rdd = spark.sparkContext.parallelize(sales_data)
+
+# Transformations to calculate discounts for each sale
+discounted_sales_rdd = rdd.map(lambda x: (x[0], x[1] * 0.9))
+print(discounted_sales_rdd.collect())
+# Output: [(1, 90.0), (2, 135.0), (3, 180.0), (4, 225.0)]
+
+# Ending the Spark Session
+
+spark.stop()