“Water, water, everywhere, nor any drop to drink”, goes the 18th-century rhyme.
“Data, data, everywhere, barely a byte refined” is the 21st-century paradigm.
Everything you do today generates data. While you’re here, reading this best courses guide, Class Central is recording information such as page views, bookmarked courses, search queries, and the time you’re spending on this page.
Now, take into account billions of users over a period of time, and we get a massive dataset!
This is where PySpark comes in, as a tool to process millions of logs quickly, clean and handle huge datasets, and analyze user behaviour to improve recommendations.
When the amount of data we deal with exceeds the growth of computing power, we start working with “Big Data”. According to Statista, 181 zettabytes (181 billion terabytes) has been generated by 2025. And as more companies dive into technologies like AI, Machine Learning, and edge computing, the more they need the skills to handle this amount of data.
How do you start or refine these skills? I’ve got the courses for you.
Why Learn PySpark?
While Spark was originally written in Scala, PySpark was developed as a Python API to allow programmers to leverage Python’s simplicity to build Spark applications. PySpark combines the distributed computing power of Spark with Python’s simplicity and readability, making it accessible even to beginners.
If you are here and wondering how you could pick up a Python API without knowing Python, don’t you worry. Check out these best picks for absolute beginners.
PySpark is used by:
- Companies like Netflix and Spotify for analyzing viewing patterns, search history, and user preferences at scale to recommend and personalize content
- Retail giants like Amazon, Walmart, Target, and Alibaba use PySpark to process enormous customer, product, and transaction datasets
- Banks, to handle high-volume, sensitive transactions securely and efficiently
If you want a powerful data skill that is beginner-friendly and trusted by top companies, PySpark will definitely give you that competitive edge.
Best PySpark Courses
Why Trust Us and Why These Courses?
Class Central is a TripAdvisor for online education. We make it easier to discover the right courses without having to jump across multiple platforms. With over 250,000 courses in our catalog, we’ve already helped more than 100 million learners find their next course.
For this course guide, I combed through Class Central’s Catalog and the internet (for reviews and recommendations by other professionals).
As an engineer starting a career in data analytics, I was on the hunt for the right courses on PySpark, and I hope my research will help you find the one best suited for you faster than I found mine.
I shortlisted these courses based on:
- Quality of Content: The curriculum and the relevance of the topics along with the lecture delivery, video quality, and the pacing of the course content were major factors for choosing these courses.
- Level of Difficulty: I’ve made sure that this list has a course for you, irrespective of your level of expertise.
- Practical Learning: Learning becomes easier when it is coupled with hands-on experience. Most of the courses in this guide include exercises, lab work, and projects.
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- Learning Material: I have checked the availability of helpful and detailed learning material within the course.
- Learner Reviews: I reviewed feedback from a diverse range of students to understand the effectiveness of the course for different types of learners.
Best Picks for Absolute Beginners
“I don’t even know Python. How can I learn PySpark?”
If this is what you were thinking, I got you. These two courses have no prerequisites at all. You can take them even if you are just a Python newbie.
Best Introduction to Big Data and PySpark (Codeacademy)
Introduction to Big Data and PySpark by Codeacademy is an excellent choice for aspiring data professionals to understand why big data matters, get a taste of real-world dataflows and learn how tools like Pyspark can utilize large-scale data using RDDs, dataframes and Spark SQL. You do not need any prior knowledge of Python to take this course.
This is a solid foundation-level course that is very easy to follow. The course consists of two modules with reading material and byte-sized lessons, followed by activities and assessments which ensure that you work with what you learn as you go.
What will you learn?
- Big data, storage and computing
- How to work with Spark Resilient Distributed Datasets (RDDs)
- How PySpark lets you do SQL-like queries on big data datasets
- How to work with a big data dataset.
| Provider | Codeacademy |
| Level | Beginner |
| Instructor | Andrea Hassler |
| No of Modules | 2 |
| Workload | 4 hours |
| Hands-On Tasks | Yes |
| Rating | 4.4 (297 ratings) |
| Cost | Paid |
Best Hands-On Course for Beginners (Coursera)
As a data engineering trainee in the past, I noticed that new learners struggle to grasp the fundamentals of distributed data processing.
This is a good introductory course that tackles this by focusing on RDDs, the core building block of PySpark. If you learn better through videos, PySpark & Python: Hands-On Guide to Data Processing by EDUCBA is the course for you!
It’s divided into two modules, beginning with the basics of Python and then moving on to programming with RDDs, data handling with MySQL, and working with PySpark joins. You can easily work through the assignments using MySQL by integrating it with JDBC as demonstrated. This course, however, does not cover dataframes and Spark SQL.
It is the first course in Spark and Python for Big Data with PySpark Specialization, which you can check out if you want to explore more of PySpark after completing the course.
What will you learn?
- The basics of Python and PySpark
- Working with RDDs (Resilient Distributed Datasets)
- Handling data with MySQL and PySpark joins
- Integrating MySQL and text processing.
| Provider | Coursera |
| Level | Beginner |
| No of Modules | 2 |
| Workload | 4 hours 21 mins |
| Hands-On Tasks | Yes |
| Rating | 4.6 (37 ratings) |
| Cost | Paid |
Courses To Check Out if You Know Python
Since PySpark is a Python API for the Spark framework, it goes without saying that knowing Python is important before delving into it. Take a look at these courses if you already work with Python.
Best Introduction to PySpark (Codesignal)
Staying engaged while learning PySpark can be a challenge, especially when you are juggling everything from the fundamentals and RDDs to dataframes and SQL, and even dipping your toes into machine learning with MLib.
Introduction to PySpark by Codesignal stands out as a truly interactive course and its interface gives learning a fun twist. With the 4 modules in this course containing video lessons along with reading material, you can learn and apply in the style that best suits you. It is like Duolingo, except this one’s for coding and there’s no green bird nagging you.
You get a futuristic Space Corgi named Cosmo instead (and no, he doesn’t nag). Cosmo makes learning more engaging and effective by clarifying concepts for you, answering your questions, and giving you additional insights on the subject.
What will you learn?
- RDDs, RDD transformations and filtering
- Working with dataframes and their operations
- Performing Spark SQL operations for queries and analysis
- Working with machine learning using PySpark MLib.
| Provider | CodeSignal |
| Level | Intermediate |
| No of Modules | 4 |
| Workload | 10 hours |
| Hands-On | Yes |
| Pre-requisites | Python |
| Rating | 4.5 (119 ratings) |
| Cost | Free |
Best Course on Big Data with Spark and Hadoop (IBM on Coursera)
In my experience as a data engineer, I have seen teams work with Spark without a solid foundational knowledge of Spark and the Hadoop ecosystem. Introduction to Big Data with Spark and Hadoop is a well-structured course by IBM that bridges this gap by combining Spark fundamentals with enterprise-level concepts like optimization engines, Kubernetes deployment, and connecting the Spark UI web server to manage processes, monitor, and debug issues.
This course blends theory and practical work well through 7 modules with short, concise videos along with lab work (ungraded) and quizzes. You will need a basic understanding of data literacy, SQL, and Python.
It is a part of the IBM Data Engineering Professional Certificate programme and NoSQL, Big Data, and Spark Foundations Specialization.
What will you learn?
- The concept, impact and use cases of big data
- Apache Spark and the Hadoop ecosystem (HDFS, MapReduce, Hive, and HBase)
- Functional programming and parallel programming using RDDs
- Dataframes, Spark SQL and ETL with dataframes
- Different types of development and runtime environments, monitoring and tuning.
| Provider | Coursera |
| Institution | IBM |
| Level | Intermediate |
| Instructor | Aije Egwaikhide, Romeo Kienzler, Rav Ahuja |
| No of Modules | 7 |
| Workload | 2 weeks (10 hours a week) |
| Pre-requisites | Basic Knowledge in Data Literacy, Python and SQL |
| Rating | 4.4 (466 ratings) |
| Cost | Paid |
Best Theoretical Course on PySpark (Udemy)
Want to make better design and performance decisions while working with Spark? Check out Learning PySpark by Packt to understand the Spark architecture and execution process.
It has 5 modules with short videos that will sharpen your conceptual understanding of distributed data processing, lazy execution, transformations, and actions to write efficient code and debug with ease.
The videos demonstrate the working of different functions, transformations, and actions used in PySpark. There are no hands-on tasks provided with the course, so if you are looking for a course with exercises/lab work, this might not be the course for you.
What will you learn?
- The Apache Spark stack, PySpark, and the Spark execution process
- Resilient Distributed Datasets (RDDs) and lazy execution
- Different PySpark transformations, and actions
- Dataframes, joins, dataframe transformations, and statistical transformations
- Data Processing with Spark dataframes.
| Provider | Udemy |
| Institution | Packt |
| Level | Intermediate |
| No of Modules | 5 |
| Workload | 2 hours 30 mins |
| Hands-On Tasks | None |
| Pre-requisites | Python |
| Rating | 4.1 (189 ratings) |
| Cost | Paid |
Best Course for Big Data Fundamentals with PySpark (Datacamp)
Big Data Fundamentals with PySpark by Datacamp is a good course to get your hands a little dirty. Many learners get stuck while applying PySpark concepts to real datasets. This course makes it easy to put theory to practice.
The course has 4 modules with video lessons and exercises with a lot of interesting datasets like Complete Works of Shakespeare and FIFA 2018. It starts from the fundamentals of PySpark and progresses into slightly more advanced topics including PySpark MLib and Machine Learning. While the video lessons follow a steady structure, the exercises are designed to guide practical learning.
Along with modules on RDDs, Spark SQL, and dataframes, this course delves into data visualization as a supporting skill for today’s market-driven industry and provides you with enough foundation to leverage PySpark MLib for machine learning tasks.
What will you learn?
- Introduction to big data analysis
- RDDs, RDD transformations, and actions
- To work with Spark SQL and dataframes
- Data visualization with PySpark using dataframes
- Machine Learning with PySpark MLib.
| Provider | Datacamp |
| Level | Advanced |
| Instructor | Upendra Kumar Devisetty |
| No of Modules | 4 |
| Workload | 4 hours |
| Hands-On Tasks | Yes |
| Pre-requisites | Python |
| Rating | 4.6 (136 ratings) |
| Cost | Paid |
Most Comprehensive Course on Pyspark (Udemy)
If you think Spark is just a data-processing tool, this course will change your mind. Big Data with Apache Spark 3 and Python: From Zero to Expert on Udemy goes beyond batch processing to demonstrate the power of Spark as a full-fledged analytics and engineering platform.
In today’s data-driven world, professionals who can harness Spark and Databricks for streaming, machine learning, and cloud integrations are in high demand across analytics, data engineering, and cloud-based roles. This course starts with an overview of big data and the basics of PySpark, gradually progressing into more diverse and advanced topics in Spark.
It offers plenty of practical exercises and their solutions along with good supplementary material which includes guides and cheatsheets. Although this is an extensive course and provides a sound understanding of how to work with Spark, it does not go in-depth into Spark architecture, performance tuning, and optimization.
Just a heads up: you need to know Python to take this course.
What will you learn?
- Spark fundamentals, Resilient Distributed Datasets (RDDs), transformations, and actions
- Dataframes, operations on dataframes, joins, Spark SQL and Koalas
- Broadcast join, caching, user-defined functions (UDF), and advanced SQL functions
- Handling missing values, schema manipulation, data visualization and persistence
- Machine Learning with PySpark and Spark streaming.
| Provider | Udemy |
| Level | Beginner |
| No of Modules | 18 |
| Workload | 5 hours 45 mins |
| Hands-On Tasks | Yes |
| Pre-requisites | Python |
| Rating | 4.0 (159 ratings) |
| Cost | Paid |
Best Picks For Projects
Best Hands-on Big Data Practices with PySpark & Spark Tuning (Udemy)
As a data professional, handling real-world big data involves challenges like skewed datasets, performance bottlenecks, and complex workflows that go beyond coding. Best Hands-on Big Data Practices with PySpark & Spark Tuning on Udemy will help you work with those on all kinds of data – be it structured, semi-structured or unstructured – using PySpark. You can easily get started if you are familiar with Python and SQL.
This project will help you understand end-to-end PySpark workflows through case studies. It provides a recap on PySpark coding. You will learn to handle data skewness and spill mitigation using optimization and performance tuning techniques, including a deep dive into Adaptive Query Execution.
The exercises in this course use real-world datasets, making you feel like you are working on corporate-level big data problems. You can experience both cloud-based and local Spark setups.
What will you work with on this project?
- A large semi-structured file, a large structured file, and a large unstructured log file with PySpark
- Spark optimization for skewed data, better performance, and using adaptive query execution
- Spark SQL using JDBC.
| Provider | Udemy |
| Level | Intermediate |
| Instructor | Amin Karami |
| No of Modules | 8 |
| Workload | 13 hours |
| Pre-requisites | Python, SQL |
| Rating | 4.5 (1,540 ratings) |
| Cost | Paid |
Best PySpark Course for End to End Real Time Project Implementation (Udemy)
Understanding PySpark in theory is one thing, but implementing a full pipeline is another. PySpark Project – End to End Real Time Project Implementation offered by Udemy takes you step by step through a production-level project. It is a perfect choice if you already have a basic knowledge of Python, HDFS, and PySpark.
This course will guide you through a complete PySpark data project workflow, all the way from setting up the project files in Pycharm to full integration testing and unit testing. Working with a US Healthcare dataset, you will understand how ingestion, preprocessing, transformation, persistence, and data transfer fit together into a real-life data processing pipeline.
As you work on this project, you will gain a lot of practical experience with industry standard best practices along with tools and technologies like YARN, Cloud, Hive, and Postgres that will notably elevate your expertise in PySpark. It also includes a crash course on HDFS and Python. If you want a taste of a professional PySpark project, this one’s got it!
What will you work with on this project?
- Single-node cluster and Spark installation
- Ingesting and preprocessing data
- Data transformation and extraction
- Data persistence and copying files from HDFS to local server/AWS S3/Azure Blob
- Full integration testing and unit testing.
| Provider | Udemy |
| Level | Intermediate |
| Instructor | Sibaram Kumar |
| No of Modules | 27 |
| Workload | 15 hours |
| Pre-requisites | Basic Knowledge in Python, HDFS and PySpark |
| Rating | 4.2 (495 ratings) |
| Cost | Paid |
Free Resources
- GitHub/pyspark-examples is a collection of open-source PySpark examples and sample projects on GitHub that demonstrate how to use the API. It is a great resource for learning by doing. You can use it as a cheat sheet of code snippets for reference. You can also modify the code for building new PySpark applications.
- Freecodecamp’s PySpark Tutorial starts from scratch and covers dataframes, aggregate functions, MLib, and implementing linear regression using Databricks in single clusters. Note that this tutorial does not include RDDs. (Duration – 1:50 hours)
Any platform that has millions of users inevitably has to handle big data. In the middle of this vast sea of information, PySpark lets you set sail smoothly into the world of distributed computing and big-data analytics. I hope this guide has nudged you toward the right resources to begin that journey.
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