What Is PySpark, and Why Should You Use It?

PySpark is an open-source application programming interface (API) for Python and Apache Spark. This popular data science framework allows you to perform big data analytics and speedy data processing for data sets of all sizes. It combines the performance of Apache Spark and its speed in working with large data sets and machine learning algorithms with the ease of using Python to make data processing and analysis more accessible.

Globally, data generation is only growing. In 2023, the world created and consumed an estimated 120 zettabytes of data, up from 97 zettabytes the year before, according to data from Statista. The global statistics platform projects that the figure will grow to 181 zettabytes by 2025 [1]. Given the essential role data plays in artificial intelligence (AI), having the ability to quickly organize, analyze quickly, and process that data gives those working in the field an advantage. That’s where PySpark excels.

Let’s examine PySpark in greater detail, along with how it compares to its competitors, jobs that commonly use it, and how you can start learning.

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 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.

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.

Who uses PySpark?

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. 

1. Big data engineer

Average annual base salary: $130,033 [2]

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.

Explore more about building a career as a big data engineer with Coursera’s 2024 Career Guide.

2. Data scientist

Average annual base salary: $120,496 [3]

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.

Discover more about what a data scientist does with Coursera’s in-depth article, What is a Data Scientist? Salary, Skills, and How to Become One

3. AI developer

Average annual base salary: $115,711 [4]

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. 

Learn more about popular jobs in AI with 6 Artificial Intelligence (AI) Jobs to Consider in 2024

4. ML engineer

Average annual base salary: $125,612 [5]

Requirements: Bachelor’s degree

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 about this career in Coursera’s What Is a Machine Learning Engineer? (+How to Get 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.

Take the next steps with Coursera.

PySpark can make it easier to perform tasks like real-time analytics, graph processing, and data preparation for use in data science, artificial intelligence, and machine learning. Continue learning about PySpark and building your skills in using it with top-rated online courses. Learn PySpark online with Coursera or explore the fields of artificial intelligence and machine learning as a whole with options like Introduction to Artificial Intelligence (AI) from IBM or the Machine Learning Specialization offered by Stanford and DeepLearning.AI.