Data transformation is a fundamental task in any data analysis or processing pipeline. In the realm of big data processing, Apache Spark has emerged as a powerful framework for handling large-scale data processing tasks efficiently. When it comes to transforming data within Spark, developers often have to choose between Spark's native functions, User-Defined Functions (UDFs), and Pandas UDFs. In this tutorial, we will explore these different approaches by using them in an example and comparing their performance, flexibility and ease of use.

Import Libraries

First, we import the following python modules:

import pandas as pd

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, lower, udf, pandas_udf
from pyspark.sql.types import StringType

Create SparkSession

Before we can work with Pyspark, we need to create a SparkSession. A SparkSession is the entry point into all functionalities of Spark.

In order to create a basic SparkSession programmatically, we use the following command:

spark = SparkSession \
    .builder \
    .appName("Python PySpark Example") \

Create PySpark DataFrame

Next, we create the PySpark DataFrame with some example data from a list. To do this, we use the method createDataFrame() and pass the data and the column names as arguments.

column_names = ["language", "framework", "users"]
data = [
    ("Python", "Django", 20000), 
    ("Python", "FastAPI", 9000), 
    ("JavaScript", "ReactJS", 5000)
df = spark.createDataFrame(data, column_names)

Native Functions

Spark provides a wide range of built-in functions that can be directly applied to DataFrames. These functions are optimized for distributed computation and are typically more efficient compared to UDFs.

In our example, we transform the column "framework" of the PySpark DataFrame to lowercase by using the lower() function of PySpark:

# Apply PySpark function to DataFrame column
df_spark = df.withColumn("framework", lower(col("framework")))

# Show DataFrame


Sometimes, built-in Spark functions might not fulfill specific requirements, and developers may need to define custom functions using User-Defined Functions (UDFs). While UDFs provide flexibility, they may not be as optimized as native functions.

In our example, we transform the column "framework" of the PySpark DataFrame to lowercase by creating a UDF and applying it to the DataFrame column:

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