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PySpark

PySpark is an open-source, Python-based library and framework for big data processing and analytics. It is part of the Apache Spark project, which is a powerful and fast cluster computing system designed for distributed data processing. It is commonly used in big data analytics, data engineering, and machine learning applications.

Radical Technologies

50,000+ English 32 hours Weekdays / Weekends Classroom / Online / Corporate

Online / Classroom

PySpark

IT Training Programme

Duration 32 hours

Batch Type Weekdays / Weekends

Mode of Training Classroom / Online / Corporate

Locations Pune, Bangalore, Kochi

Language English

Certification Globally Recognized

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What you'll learn

Understand core concepts and architecture from the ground up

Get hands-on with the tools used by working professionals

Build real-world projects you can add to your portfolio

Learn industry best practices and coding standards

Practice with real datasets and real-world scenarios

Prepare for certification and technical interviews

Work on collaborative, team-based exercises

Apply performance tuning and optimization techniques

Understand how the technology fits into a larger ecosystem

Complete assignments reviewed by mentors

Programme Overview

8 sections covering the complete curriculum — a single, progressive learning arc.

32 hours

Training Duration

Core Modules

Total Lessons

4.9★

Average Rating

50K+

Students Trained

Foundations & Core Concepts

Get hands-on with the fundamentals and architecture — the building blocks for everything that follows.

Fundamentals Architecture Setup

Hands-On Practical Training

Work through real exercises and assignments designed to mirror what you will do on the job.

Practicals Assignments Labs

Real-World Projects

Apply what you have learned to end-to-end projects that go straight into your portfolio.

Projects Portfolio Case Studies

Advanced Techniques

Go beyond the basics with advanced concepts, integrations and production-grade practices.

Advanced Integration Best Practices

Ecosystem Integration

Understand how this technology connects with the broader tools and platforms used in the industry.

Ecosystem Tools Platforms

Performance & Interview Prep

Master optimization techniques and prepare for the technical interview questions employers actually ask.

Optimization Interview Prep Certification

Who is this programme for?

Whether you're already writing code, working with data, or supporting applications today — this programme is built to take you into a BIGDATA role.

Software Developers

Engineers who want to add this skill set to their toolkit

Analysts & Consultants

Professionals moving into a more technical, hands-on role

IT Professionals

System admins and support engineers upskilling into a new domain

Fresh Graduates

CS/IT graduates aiming for a job-ready technical role

Course Curriculum

8 sections • 47 lessons • 32 hours

Jump to Section

Total Duration

32 hours

100% Practical · Unlimited Doubt Sessions

01 Module 1: Introduction to PySpark

What is PySpark?

• PySpark vs. Spark: Understanding the difference

• Spark architecture and components

• Setting up PySpark environment

• Creating RDDs (Resilient Distributed Datasets)

• Transformations and actions in RDDs

• Hands-on exercises

02 Module 2: PySpark DataFrames

• Introduction to DataFrames

• Creating DataFrames from various data sources (CSV, JSON, Parquet, etc.)

• Basic DataFrame operations (filtering, selecting, aggregating)

• Handling missing data

• DataFrame joins and unions

• Hands-on exercises

03 Module 3: PySpark SQL

• Introduction to Spark SQL

• Creating temporary views and global temporary views

• Executing SQL queries on DataFrames

• Performance optimization techniques

• Working with user-defined functions (UDFs)

• Hands-on exercises

04 Module 4: PySpark MLlib (Machine Learning Library)

• Introduction to MLlib

• Data preprocessing and feature engineering

• Building and evaluating regression models

• Classification algorithms and evaluation metrics

• Clustering and collaborative filtering

• Model selection and tuning

• Hands-on exercises with real-world datasets

05 Module 5: PySpark Streaming

• Introduction to Spark Streaming

• DStream (Discretized Stream) and input sources

• Windowed operations and stateful transformations

• Integration with Kafka for real-time data processing

• Hands-on exercise

06 Module 6: PySpark and Big Data Ecosystem

• Overview of Hadoop, HDFS, and YARN

• Integrating PySpark with Hadoop and Hive

• PySpark and NoSQL databases (e.g., HBase)

• Spark on Kubernetes

• Hands-on exercises

07 Module 7: PySpark Optimization and Best Practices

• Understanding Spark’s execution plan

• Performance tuning and optimization techniques

• Broadcast variables and accumulators

• PySpark configuration and memory management

• Coding best practices for PySpark

• Hands-on exercises

08 Module 8: Advanced PySpark Concepts (Optional)

• Spark GraphX for graph processing

• SparkR: R language integration with PySpark

• Deep learning with Spark using TensorFlow or Keras

• PySpark and SparkML integration

• Hands-on exercises and mini-projects

Tools & Technologies

Every tool listed here is installed, configured and used in a hands-on lab session.

Core Tools

Hands-On Labs

Practical Environment

Industry-Standard Tools

Real-World Setup

Guided Exercises

Skill Building

Sample Datasets

Practice Material

Practice & Projects

Mini Projects

Applied Practice

Assignments

Mentor Reviewed

Doubt Sessions

Live Support

Career Readiness

Resume Building

Career Support

Mock Interviews

Interview Prep

Certification Prep

Global Recognition

Deployment & Delivery

Production Practices

Real-World Ready

Best Practices

Industry Standards

47+

Hands-On Lessons

Core Modules

32 hours

Training Duration

100%

Practical Training

You don't just learn PySpark. You ship it.

Three major projects, each mirroring how production teams actually work — from guided foundations to a portfolio-ready capstone.

PROJECT // 01

Guided Foundation Project

→Requirement Analysis

→Guided Implementation

→Mentor Review

→Iteration

Foundation Beginner

Apply the fundamentals in a structured, mentor-reviewed project

Take the core concepts from the first half of the curriculum and apply them to a realistic scenario, with guidance and feedback from your mentor at every step.

Structured project brief

Step-by-step implementation

Mentor feedback and review

Documented outcome

Stack Core Concepts Best Practices

PROJECT // 02

Applied Practice Project

→Scenario Design

→Independent Build

→Testing & Validation

→Peer Review

Applied Intermediate

Build a more independent project mirroring real production scenarios

Work through a project that combines multiple concepts from the curriculum, closer to how work is actually structured on the job — less hand-holding, more ownership.

End-to-end implementation

Testing and validation

Documentation

Peer/mentor review

Stack Applied Skills Testing

PROJECT // 03

Capstone Project

→Planning

→End-to-End Build

→Review & Refinement

→Presentation

Capstone Advanced

Take a project from requirements to a polished, portfolio-ready deliverable

Your final project — plan, build, test and present a complete solution using everything covered in the curriculum, reviewed by mentors before you graduate.

Complete working solution

Presentation-ready documentation

Mentor sign-off

Portfolio-ready deliverable

Stack Full Curriculum Portfolio

All 3 projects go directly into your portfolio & resume — reviewed by mentors before you graduate.

See Sample Project Reports

Upcoming Batches

No upcoming batches scheduled right now. Enquire to get notified.

Why Radical Technologies

Live Online Training

Highly practical oriented training
Installation support on your system
24/7 Email and Phone support
100% Placement Assistance
Global Certification Preparation
Trainer-Student Interactive Portal
Assignments and Projects by Mentors

Live Classroom Training

Weekend / Weekdays / Morning / Evening batches
80:20 Practical and Theory ratio
Real-life Case Studies
Easy make-up for missed sessions
PSI | Kryterion | Redhat Test Centers
Lifetime Video Classroom Access (coming soon)
Resume Prep and Mock Interviews

Self-Paced Training

Learn 300+ courses at your own time
50,000+ Satisfied Learners
Course Completion Certificate
Practical Labs available
Mentor Support available
Doubt Clearing Session available
10% Discounted Global Certification

Like the Curriculum? Let's Get Started

Join 50,000+ students already enrolled at Radical Technologies

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Global Certification

Radical Technologies is the leading IT certification institute in Pune, offering globally recognized certifications across various domains. With expert trainers and comprehensive materials, we ensure students gain in-depth knowledge and hands-on experience to excel in their careers. Our certification programs are tailored to meet industry standards — from cloud technologies to data science — empowering individuals to stay ahead in the ever-evolving tech landscape.

Career Services

At Radical Technologies, we are committed to your success beyond the classroom. Our 100% Job Assistance program ensures that you are not only equipped with industry-relevant skills but also guided through the job placement process. With personalised resume building, interview preparation, and access to our extensive network of hiring partners, we help you take the next step confidently into your IT career.

Career Support

Course Completed? Need next steps?

Need Interview Supports?

Need Job Assistance?

Came from any other Institute?

Join our Brush-up Session & get support until you find a job!

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Radical Learning Eco-System

Exam Simulator

Cloud SandBox

Hands-on Cloud Lab

Developer Coding Ground

Student Reviews

4.9★

Average learner rating

50K+

Students trained

30+

Hiring companies alumni work at

100%

Placement assistance

4.9

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Course Rating

★★★★★

92%

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Enrolling in the PySpark Classes in Bengaluru was one of the best decisions I made. The course helped me gain a deep understanding of distributed computing and PySpark, and I now feel prepared to take on big data projects in my career.

S Satisfied Student

The PySpark Course in Bengaluru at Radical Technologies was exactly what I needed to move forward in my data engineering career. The course material was practical, and the instructors were highly supportive.

S Satisfied Student

The PySpark Online Certification in Bengaluru was very well-organized, and the online format allowed me to learn at my own pace. The certification process was smooth, and I now have the confidence to work with big data.

S Satisfied Student

I took the PySpark Corporate Training in Bengaluru for my team, and the experience was great. The instructors understood our business requirements and provided customized content that helped us implement PySpark effectively in our organization.

S Satisfied Student

The PySpark Certification in Bengaluru was an excellent investment in my career. The course was thorough, and the certification has opened up new opportunities for me in the data science field.

S Satisfied Student

I highly recommend Radical Technologies for PySpark Training in Bengaluru. The instructors provided real-time support, and I got hands-on experience working with PySpark, which has greatly enhanced my data analytics skills.

S Satisfied Student

The PySpark Online Course in Bengaluru was incredibly well-structured and provided me with the knowledge needed to excel in the world of big data. The online format was perfect for someone with a busy schedule like mine.

S Satisfied Student

Attending the PySpark Online Training in Bengaluru helped me sharpen my skills in big data technologies. The instructors provided valuable feedback, and the course material was engaging and practical.

S Satisfied Student

The PySpark Online Classes in Bengaluru were incredibly convenient, allowing me to balance my work and learning. The course provided deep insights into Spark and its applications, and the online format made it easy to study from anywhere.

S Satisfied Student

I enrolled in the PySpark Certification in Bengaluru and was impressed by the comprehensive curriculum. The certification has added significant value to my resume, and I now have a strong understanding of big data technologies.

S Satisfied Student

Radical Technologies offers the best PySpark Institute in Bengaluru. The trainers were very approachable and always available for support. I’m now confident in my ability to work with PySpark for data processing and machine learning.

S Satisfied Student

I took the PySpark Course in Bengaluru, and the learning experience was exceptional. The course content was up-to-date, and the hands-on projects gave me practical experience with real-world data problems.

S Satisfied Student

The PySpark Corporate Training in Bengaluru helped our team quickly get up to speed with Spark. The content was tailored to our specific requirements, making it highly relevant to our business needs.

S Satisfied Student

I highly recommend Radical Technologies for anyone looking for PySpark Classes in Bengaluru. The course is hands-on, and the instructors are highly knowledgeable. I now feel prepared to work with Spark in my career.

S Satisfied Student

The PySpark Training in Bengaluru provided me with a solid foundation in PySpark, and I gained a clear understanding of how to use Spark for large-scale data analysis. The real-time examples made learning fun and impactful.

S Satisfied Student

Enrolling in the PySpark Online Certification in Bengaluru was one of the best decisions I made. The certification process was seamless, and I now have the skills to work on advanced data processing tasks.

S Satisfied Student

The PySpark Online Training in Bengaluru helped me build expertise in Spark and Hadoop. I now feel comfortable using PySpark for data analysis and machine learning tasks. The online learning environment was engaging and well-organized.

S Satisfied Student

The PySpark Online Course in Bengaluru exceeded my expectations. I gained hands-on experience with big data tools, and the course helped me advance my career in data science. The online format was convenient and highly effective.

S Satisfied Student

I took the PySpark Online Classes in Bengaluru and was pleasantly surprised by the quality of the content. The flexibility of online learning allowed me to study at my own pace, and the support from trainers was exceptional.

S Satisfied Student

I attended the PySpark Corporate Training in Bengaluru, and it was tailored perfectly to our team's needs. The corporate-focused sessions were interactive and provided us with a deep understanding of how to apply PySpark in business environments.

S Satisfied Student

Thanks to the PySpark Training in Bengaluru, I gained practical experience working with real-world data. The instructors provided excellent support throughout the course, making it easier to understand complex concepts.

S Satisfied Student

Radical Technologies is the best PySpark Institute in Bengaluru. The training is thorough, and the hands-on projects helped me learn how to apply PySpark concepts effectively. This course has enhanced my career prospects significantly.

S Satisfied Student

The PySpark Classes in Bengaluru provided me with a comprehensive understanding of distributed computing. The curriculum is well-structured, and the faculty’s expertise is unmatched. I am now able to implement PySpark in my current job.

S Satisfied Student

I enrolled in the PySpark Certification in Bengaluru and was extremely impressed by the course content and delivery. The learning experience was enriching, and I feel confident in my ability to handle big data challenges now.

S Satisfied Student

The PySpark Course in Bengaluru at Radical Technologies was a game-changer for me. The practical hands-on approach and expert instructors helped me gain in-depth knowledge of PySpark. Highly recommend this institute for anyone looking to build a solid foundation in big data.

S Satisfied Student

Frequently Asked Questions

15 questions about the PySpark course.

01 What is PySpark?

PySpark is the Python API for Apache Spark, a powerful open-source framework for distributed computing. It allows Python developers to harness the power of Spark’s parallel processing and distributed systems, enabling efficient data processing, machine learning, and real-time analytics.

02 How does PySpark work with big data?

PySpark handles big data by dividing large datasets into smaller partitions that are processed in parallel across a cluster. This distributed computing approach speeds up tasks such as data analysis, transformation, and machine learning, even with massive datasets.

03 What are RDDs in PySpark?

Resilient Distributed Datasets (RDDs) are the fundamental data structure in PySpark, representing an immutable, distributed collection of objects. RDDs allow for parallel operations across a cluster, supporting fault tolerance and the ability to scale with large datasets.

04 What is the difference between PySpark’s RDDs and DataFrames?

RDDs are the lower-level data structure in PySpark, giving fine-grained control over data. DataFrames, on the other hand, are higher-level abstractions built on top of RDDs, offering optimized execution and easier integration with SQL operations. DataFrames are more efficient and easier to work with for structured data.

05 What is lazy evaluation in PySpark?

Lazy evaluation in PySpark means that transformations on data (such as map() or filter()) are not executed immediately but rather when an action (such as collect() or count()) is called. This delay allows Spark to optimize the execution plan, improving performance by reducing unnecessary computations.

06 How do you handle missing data in PySpark?

In PySpark, missing data can be handled using methods like fillna() to replace missing values, dropna() to remove rows with missing values, or replace() to substitute specific values. These functions allow for flexible handling of incomplete datasets during data processing.

07 What are the types of transformations in PySpark?

There are two types of transformations in PySpark:
Narrow transformations: Operations like map() and filter() that only involve a single partition of data.
Wide transformations: Operations like groupByKey() and join() that require data shuffling between partitions.

08 How does PySpark perform data partitioning?

PySpark partitions data based on the number of available nodes in the cluster and the data’s characteristics. Partitioning ensures parallel processing, improving performance by distributing tasks across different workers. Partitioning can be optimized using repartition() or coalesce() for better resource utilization.

09 What are PySpark Accumulators?

Accumulators are variables that allow tasks to accumulate values in a fault-tolerant manner across Spark jobs. They are primarily used for counters and sums during distributed computations, and their final values can be retrieved from the driver node after the job is complete.

10 What is a Broadcast Variable in PySpark?

A Broadcast Variable allows large read-only data to be shared across all worker nodes in a Spark cluster. Broadcasting reduces the overhead of shipping data to each node and is useful for small datasets that need to be referenced across multiple operations.

11 How does PySpark handle real-time data processing?

PySpark handles real-time data processing through Spark Streaming. It processes data in small batches (micro-batches) for real-time applications such as monitoring, sensor data analysis, or fraud detection. Spark Streaming can process data from sources like Kafka, Flume, and HDFS.

12 How do you optimize the performance of PySpark applications?

Performance can be optimized in PySpark by:
Using DataFrames instead of RDDs for better execution optimization.
Caching intermediate datasets when necessary to avoid recomputation.
Adjusting the number of partitions to balance workload.
Avoiding wide transformations that cause unnecessary shuffling of data.
Using built-in functions for faster operations instead of custom UDFs.

13 What is PySpark SQL?

PySpark SQL is a module in PySpark that enables users to run SQL queries on structured data. It provides a programming interface for working with DataFrames, and it allows SQL syntax to be used for complex queries, making it easier to handle large datasets using familiar SQL operations.

14 What is the difference between collect() and take() in PySpark?

collect(): Retrieves the entire dataset from the Spark cluster to the driver node. It should be used cautiously with large datasets as it can cause memory overload.
take(): Returns a specified number of elements from the dataset, making it more suitable for inspecting a sample of the data without pulling the entire dataset into memory.

15 Can PySpark integrate with other big data technologies?

Yes, PySpark integrates seamlessly with other big data technologies such as Hadoop (HDFS), Hive, Kafka, and Cassandra. It also supports a range of connectors for cloud storage services like AWS S3 and Azure Blob Storage, making it versatile for various data storage and processing scenarios.

PySpark Interview Questions

10 questions commonly asked in PySpark interviews.

01 Describe the PySpark architecture and explain the roles of the Driver, Executors, and Cluster Manager.
02 How does PySpark handle data partitioning, and why is partitioning important in distributed computing?
03 What is lazy evaluation in PySpark, and how does it impact the performance of Spark applications?
04 Explain the difference between RDDs, DataFrames, and Datasets in PySpark. When should you use each?
05 What are the key components of PySpark, and how do they contribute to big data processing?
06 What is Spark Streaming, and how does PySpark handle real-time data processing with it?
07 What are accumulators and broadcast variables in PySpark, and how are they used in Spark programs?
08 Explain how PySpark integrates with Hadoop Distributed File System (HDFS) and other storage systems.
09 How can you optimize PySpark applications to improve performance in large-scale data processing?
10 What are narrow and wide transformations in PySpark, and how do they affect the execution of Spark jobs?

PySpark Course Certification with Training in Pune

Radical Technologies is the leading institute in Bangalore for PySpark Course Training, offering top-notch education in the field of big data and distributed computing. Located in the heart of Bengaluru, we provide a comprehensive range of courses tailored to meet the demands of professionals and organizations looking to enhance their skills in Apache Spark and PySpark. Our PySpark Course in Bengaluru is designed to equip students with the practical knowledge and expertise needed to excel in the rapidly evolving data science and big data industries.

As the go-to PySpark Institute in Bengaluru, we offer a variety of training options to cater to different learning preferences. Whether you’re looking for PySpark Certification in Bengaluru to boost your career or prefer the flexibility of PySpark Online Classes in Bengaluru, our courses are designed to fit your needs. Our PySpark Classes in Bengaluru are taught by experienced instructors who provide real-time project-based learning, ensuring that you gain hands-on experience and a deep understanding of PySpark concepts.

At Radical Technologies, we are committed to providing high-quality PySpark Training in Bengaluru with a focus on real-world applications. Our PySpark Corporate Training in Bengaluru helps organizations upskill their teams, empowering them with the knowledge needed to leverage PySpark for big data processing. Additionally, for professionals who prefer learning at their own pace, we offer convenient PySpark Online Training in Bengaluru and PySpark Online Courses in Bengaluru, with flexible schedules and accessible content.

We also provide a structured PySpark Online Certification in Bengaluru program that enables students to earn a recognized certification upon successful completion of the course, further enhancing their job prospects in the competitive big data landscape.

Choose Radical Technologies for a world-class PySpark Course in Bengaluru and take the first step towards mastering PySpark and unlocking your potential in the world of big data.