What Is PySpark, and Why Should You Use It?

Key takeaways

• PySpark is a Python API for Apache Spark that lets you process and analyze data at scale, making it a powerful choice for anyone working with large or complex datasets.

• You can use PySpark to support distributed computing, in‑memory processing, and fault‑tolerant workflows, enabling fast analysis of massive data sets across industries like finance, retail, and health care.

• PySpark in Python is Spark’s Python interface, giving users access to DataFrames, MLlib, Spark SQL, and streaming capabilities so they can run big‑data workflows without switching languages.

Understanding Spark and PySpark helps clarify how PySpark builds on Spark’s distributed engine to give Python users a scalable way to work with large data sets. This article examines PySpark in more detail, compares it with competitors, covers common use cases, and explains how to get started.

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?

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

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 to and from a website, such as retrieving weather information or posting content on social media. APIs simplify certain functions, making it easier to create complex code.

What is PySpark used for?

PySpark lets you use Apache Spark’s speed to process data at scale, including the massive volumes common in big‑data workloads. 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 such as YARN, Spark, and Mesos, the latter of which retired and moved to the Apache Attic in August 2025.

What are some PySpark alternatives?

While PySpark is a popular tool among machine learning (ML) 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 but can also 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 Dataflow: This is a managed service for stream and batch processing that lets teams build ETL, analytics, and real‑time pipelines through a unified model. It handles scaling, resource management, and fault‑tolerant execution while integrating with Google’s data tools to support efficient processing of large, diverse data sets.

• Apache Flink: A distributed engine built for fast, stateful stream processing, with unified handling of streaming and batch data, event‑time accuracy, and scalable deployments for real‑time analytics and continuous pipelines.

• 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 require PySpark include the following.

*All salary information represents the median total pay from Glassdoor as of September 2026. These figures include base salary plus profit-sharing, bonuses, commissions, and other forms of additional compensation.

1. Big data engineer

Median total salary: $147,000 [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 building data management systems. You’ll use PySpark to prepare and clean data and develop machine learning models.

2. Data scientist

Median total salary: $159,000 [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 help businesses and decision-makers leverage data-driven insights. PySpark can help you with tasks like graph processing and SQL queries.

3. AI developer

Median total salary: $161,000 [3]

Requirements: Typically need a bachelor’s degree

In this role, you'll integrate AI into software, implement algorithms, and work with data and data architecture 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

Median total salary: $155,000 [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 challenges of using PySpark?

PySpark offers numerous advantages and some limitations. For example, with PySpark, you use built‑in functions that handle complex data partitioning so you can focus on the rest of your task. It also offers the speed of Apache Spark through a Python-based interface, reducing the learning curve if you're familiar with Python. It also offers numerous features that let you analyze even massive amounts of data quickly and efficiently.

Some limitations include complex debugging. PySpark often shows errors in Python code and Java stack traces, making the process more intricate. 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. While Spark still supports RDDs, developers now typically use them for lower‑level operations and tend to rely on DataFrames for more modern PySpark workflows.

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