What Is PySpark, and Why Should You Use It?

Key takeaways

PySpark is an open-source application programming interface (API) for Python and Apache Spark that lets you analyze data sets of all sizes. 

• In popular careers using PySpark, you may earn an average of $143,000 to $157,000, depending on the role [1, 2, 3, 4]

• In addition to supporting data processing and analysis, PySpark also supports Apache Sparks features, such as SparkSQL and its machine learning library. It’s also compatible with external libraries like GraphFrames to help make analyzing graphs more efficient. 

• After learning PySpark and earning the proper credentials, you can pursue various careers, including big data or machine learning engineer, data scientist, or artificial intelligence (AI) developer.

Examine PySpark in greater detail, along with how it compares to its competitors, jobs that commonly use it, and how you can start learning. If you want to build a career using PySpark and other tools, consider enrolling in the Data Analysis and Visualization Foundations Specialization from IBM. Throughout this four-course series, you'll have an opportunity to learn other tools, including Apache Hadoop and Hive, Microsoft Excel, and IBM Cognos Analytics. You may also develop a strong foundation in data cleansing, analysis, and visualization. 

What is PySpark?

This collaboration between Python and Apache Spark facilitates data processing and analysis, even for massive data sets. It supports Apache Spark's various features, including its machine learning library (MLlib), DataFrames, and SparkSQL. Using PySpark, you can also transition between Apache Spark and Pandas, perform stream processing and streaming computation, and interface with Java virtual machine (JVM) objects. It is compatible with external libraries, including GraphFrames, which is valuable for efficient graph analysis, and PySparkSQL, which makes tackling massive amounts of data easier. 

What is the purpose of a Python API?

The purpose of a Python API is to allow different software programs to communicate with each other using Python. For example, a web API might let your Python program send and receive data from a website, like pulling weather information or posting content on social media. APIs simplify certain functions so you can create complex code more easily.

What is PySpark used for?

PySpark makes it possible to harness the speed of Apache Spark while processing data on data sets of any size, including massive sizes associated with big data. You can analyze data interactively using the PySpark shell, with performance that’s exponentially faster than if you did it in Python alone. It offers various features, including in-memory computation, fault tolerance, distributed processing, and support for cluster managers like Yarn, Spark, and Mesos, which retired and moved to the Attic in August 2025.

What are some PySpark alternatives?

While PySpark is a popular tool among machine learning professionals and data scientists, you have other options to consider. The list below offers a brief synopsis of a few popular PySpark alternatives.

• Dask: This Python framework primarily supports Python only but will work with Python-linked code in languages like C++ and Fortran. It offers lighter weight and more flexible performance but lacks PySpark’s all-in-one capabilities.

• Google Cloud Platform: It provides a serverless, autoscaling option to work with Spark while integrating with Google's array of tools. While PySpark primarily aims to aid DevOps teams, the Google Cloud Platform's robust list of features serves IT professionals, developers, and users of all types. You can use it to work with big data, machine learning, AI, and other computing tasks.

• Polars: This open-source performance-focused data wrangling solution offers fast installation and support for various data formats, including CSV, JSON, Feather, MySQL, Oracle, Parquet, Azure File, and more. It is a Rust-based solution that relies on Apache Arrow's memory model, enhancing your ability to integrate it with other data tools you're using.

What is PySpark used for in industry? Related careers

Companies like Walmart, Runtastic, and Trivago report using PySpark. Like Apache Spark, it has use cases across various sectors, including manufacturing, health care, retail, and finance. 

Those using it typically work in machine learning and data science. Four careers you might encounter that often include PySpark as a required skill include the following. 

*All salaries sourced from Glassdoor in December 2025 and represent total median pay, which includes base salary plus profit-sharing, bonuses, commissions, and other forms of additional compensation.

1. Big data engineer

Average annual salary: $143,000 [1]

Requirements: Bachelor’s degree at a minimum

As a big data engineer, you'll perform diverse tasks, including developing and designing algorithms and predictive models, innovating ways to improve data quality, and developing data management systems. You’ll use PySpark to prepare and clean data and develop machine learning models.

2. Data scientist

Average annual salary: $153,000 [2]

Requirements: Bachelor’s degree at a minimum

As a data scientist, you might work in various fields, including finance, health care, and retail environments. You'll use tools like PySpark, among others, to analyze data and aid businesses and decision-makers in leveraging data-driven insights. PySpark can help you with tasks like graph processing and SQL queries.

3. AI developer

Average annual salary: $157,000 [3]

Requirements: Typically need a bachelor’s degree

In this role, you'll essentially work to integrate AI into software, implement algorithms, and work with the data and data architecture necessary to inform various projects. Given Apache Spark and Python’s roles in AI and machine learning, developing skills working with PySpark can be valuable in helping you in this career. 

4. ML engineer

Average annual salary: $148,000 [4]

Requirements: Bachelor’s degree at a minimum

Working with data is integral to your tasks as a machine learning engineer. You will work closely with others, including data scientists, to develop algorithms, evaluate models, and turn unstructured data into valuable insights. You’ll likely use PySpark to prepare data, build ML models, and train them.

Read more: Machine Learning Skills: Your Guide to Getting Started

What are the benefits and drawbacks of using PySpark?

As previously covered, PySpark offers numerous advantages. For example, with PySpark, complex functions for data partitioning are automated, allowing you to focus on other aspects of the task you're working on. It also offers the speed of Apache Spark but is easier to use if you're familiar with Python. That means it boasts a limited or nonexistent learning curve. It also offers numerous features that make analyzing even massive amounts of data possible quickly.

The disadvantages include complicated debugging. PySpark often shows errors in Python code and Java stack, making the process more complex. Finding data quality issues can also be challenging, particularly with large-scale data sets.

How can you get started in PySpark?

Before using PySpark, you must install and become familiar with Python, Jupyter Notebook, Java, and Apache Spark. At this point, you can install PySpark and begin working with it. Online tutorials and courses can help you learn how to read files, complete data analysis, and use PySpark for machine learning. As you become proficient in working with PySpark, you'll be able to execute commands, convert resilient distributed data sets (RDDs) into data frames, organize data, and work with large-scale data sets for various projects.

What is RDD in PySpark?

An RDD, or resilient distributed data set, in PySpark is a fundamental data structure used to store and process data across multiple computers. It allows Spark to work with large data sets by dividing them across machines and performing operations in parallel. RDDs can come from existing files, can be saved for reuse, and are fault-tolerant, meaning you can recover your data if a computer fails.

Explore our free data science resources

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• Watch a YouTube video: How to Start a Data Science Career

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• Learn from an expert: 6 Questions with an IBM Data Scientist and AI Engineer

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