PySpark and Data Engineering Training

PySpark Online Course with Certification

Learn PySpark online with live trainer support. The course covers Python, DataFrames, Spark SQL, ETL pipelines, Databricks, streaming, Delta Lake and Airflow. Choose live online, classroom or self paced learning. You receive a certificate of completion.

★ 4.3 out of 5 rating 15k+ learners trained Certificate on completion

Live Interactive Classes
Job Assistance
Industry Expert Trainers
Recorded Classes on LMS
Hands On Practicals
Online and Classroom Batches

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Course Duration

[Confirm duration]

Learning Mode

Live Online, Classroom and Self Paced

Projects

Capstone Industry Project

Certification

Certificate of Completion

Job Support

Job Assistance

Batch Timing

[Confirm batch timings]

Experience Level

Beginner Friendly

Fees Start At

₹5,000

Who Should Take This PySpark Course?

This course is for:

Beginners

Start with Python and Linux basics before you move into Spark.

Software Developers

Add big data processing to your development skills.

Data Analysts

Move from small files to large datasets with PySpark and Spark SQL.

Aspiring Data Engineers

Build ETL pipelines, streaming jobs and scheduled workflows.

Not sure which course is right for you?

Take our 2 minute course finder to choose between PySpark, Big Data, Apache Spark Scala and Python.

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Learning PySpark from Scratch: What You Will Learn

Spark Fundamentals Python and Linux RDDs DataFrames and Spark SQL ETL and File Formats Performance Tuning Streaming and Delta Lake Airflow and Capstone

By the end of the course, you'll be able to:

Explain Spark architecture, including the driver, executors, DAG and lazy evaluation
Write Python code for PySpark, including functions, classes, exception handling and logging
Work with RDDs, transformations, actions, caching and shared variables
Process data with DataFrames and Spark SQL, including views and user defined functions
Build ETL pipelines with data cleansing, deduplication, incremental loads and change data capture
Read and write CSV, JSON, Avro, Parquet and ORC files with partitioning and bucketing
Handle joins, window functions and data skew, and tune jobs with the Spark UI and query execution plans
Build streaming jobs with Kafka, use Delta Lake for ACID transactions and schedule pipelines with Apache Airflow

PySpark tools and technologies

Python

PySpark

Spark SQL

Databricks

Kafka

Delta Lake

Apache Airflow

Git

Hands On PySpark Projects

Project 01

Capstone Industry Data Pipeline

Tools: PySpark, Spark SQL, cloud storage

You'll build:

A pipeline on retail, banking, telecom or healthcare data
PySpark ETL from data source to cloud storage
Data processing with DataFrames and Spark SQL
An analytics dashboard

View Project →

Practical 02

ETL and Performance Tuning Practicals

Tools: PySpark, Spark UI

You'll build:

Data cleansing and deduplication steps
Incremental loads and change data capture
Joins, window analytics and data skew handling
Jobs tuned with query execution plans

View Practical →

Practical 03

Streaming, Delta Lake and Airflow Practicals

Tools: Structured Streaming, Kafka, Delta Lake, Airflow

You'll build:

Streaming jobs with Kafka
Watermarking and checkpointing
Delta Lake merge, upsert and time travel
Airflow DAGs to schedule and monitor pipelines

View Practical →

Each project and practical gives you something specific to explain in an interview.

PySpark Career Roles After This Course

Course

Skill Building

Hands On Practicals

Capstone Project

CV Help

Interview Preparation

Job Assistance

Career roles you can target

Role	Core Skills
PySpark Developer	PySpark, DataFrames, Spark SQL, performance tuning
Junior Data Engineer	ETL pipelines, Delta Lake, Airflow, Kafka basics
ETL Developer	Data cleansing, incremental loads, change data capture, file formats
Big Data Developer	Spark, Structured Streaming, Databricks
Data Analyst for Large Data	Spark SQL, joins, window functions

Job assistance is described below with what it includes. These roles are examples of where the skills are used. This course does not guarantee a job offer.

How Prwatech supports your job search

What job assistance includes, in detail.

01

CV Help

Get help creating a professionally written CV.

02

Interview Preparation

Get guidance for interview preparation and common interview questions.

03

Capstone Project

Work on an industry project to see how data teams build pipelines.

04

Screening Test

Learners who pass the screening test receive interview calls.

05

Job Assistance

[Describe exactly what else this includes, such as referrals or a job board.]

92% learners placed since 2010

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Why Learn PySpark with Prwatech

15+ Years

Training Experience

15k+

Learners Trained

Industry

Experts Lead Live Q&A

Recorded

Classes on Your LMS

Job

Assistance Included

Online +

Classroom Batches

PySpark Trainers

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[Trainer Name]

[Current role]

[Years of experience]

Specialization: [Specialization]

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[Trainer Name]

[Current role]

[Years of experience]

Specialization: [Specialization]

Meet Your Trainers →

Student Reviews

★ 4.3 average from 299+ verified Google reviews

Every review is public on Google.

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Before → After

Before	After
No Spark knowledge	PySpark DataFrames and Spark SQL
No pipeline experience	ETL pipelines with validation and error handling
No streaming experience	Structured Streaming with Kafka
No cloud experience	Databricks and AWS EMR basics

PySpark Course Syllabus

1. Spark Fundamentals, Python and Linux

Evolution of big data and the Hadoop ecosystem
Spark compared with Hadoop MapReduce, and the Spark core components
Cluster managers (Standalone and YARN) and Spark deployment modes
Spark architecture: driver, executor and worker nodes, DAG and lazy evaluation
Python basics, functions, lambda expressions, object oriented concepts and collections
Exception handling, file handling, virtual environments and logging in Python
Linux commands, file permissions, shell scripting basics, and Git fundamentals with branching, merge and the GitHub workflow

2. Spark RDD Programming

RDD fundamentals and creating RDDs
Parallelization
Transformations and actions, narrow and wide transformations
Pair RDD operations
Persistence and caching
Shared variables: broadcast and accumulator

3. DataFrames and Spark SQL

Introduction to DataFrames and schema inference
DataFrame operations
Spark SQL architecture
Temporary and global views
Complex queries
User defined functions

4. Data Processing, ETL Pipelines and File Formats

ETL concepts, data cleansing, handling missing data and deduplication
Incremental loads, change data capture and data validation
Pipeline design patterns, error handling and retry mechanism
CSV, JSON, Avro, Parquet and ORC, and compression techniques
HDFS fundamentals, partitioning and bucketing
Reading and writing optimized files, with Amazon S3 and Azure Blob integration

5. Advanced Transformations and Performance Optimization

Joins and join strategies, including broadcast joins
Aggregations, pivot and unpivot, and window analytics
Complex transformations and data skew handling
Spark UI, query execution plan and analysing DAG execution
Catalyst optimizer and Tungsten engine
Partition and shuffle optimization
Memory management, caching strategies and performance tuning best practices

6. PySpark with Cloud and Databricks

Introduction to Databricks and the Databricks workspace
Notebooks and clusters
AWS EMR
AWS Glue basics
Azure Databricks
Job scheduling

7. Streaming, Delta Lake and Lakehouse

Structured Streaming and streaming architecture
Kafka basics and Kafka integration with Spark
Real time ETL, watermarking and checkpointing
Data lake and data warehouse, and lakehouse architecture
Delta Lake features and ACID transactions
Time travel, merge and upsert

8. Airflow and Capstone Project

Apache Airflow basics and DAG creation
Scheduling pipelines and monitoring jobs
Alerts and notifications
An industry project in retail analytics, banking transactions, telecom logs or healthcare data
Project flow: data source, PySpark ETL, cloud storage, data processing and analytics dashboard

PySpark Course Fees and Learning Formats

All formats include the same curriculum and projects.

Swipe sideways to compare all three formats →

	Self Paced	Most Popular Live Online	Classroom
Recorded Content	✓	✓	✓
Live Classes	Not included	✓	✓
Trainer Support	Limited	✓	✓
Projects	✓	✓	✓
Career Support	[Specify]	✓	✓
Access	Lifetime	[Duration]	[Duration]
Price	₹5,000 or ₹[X,XXX] per month EMI	₹[XX,XXX] or ₹[X,XXX] per month EMI	₹[XX,XXX] or ₹[X,XXX] per month EMI

Talk to an Advisor

What happens after you enquire?

1

Submit enquiry

2

Advisor calls you

3

Discuss background & goals

4

Choose suitable batch

5

Complete enrollment

6

Start learning

Frequently Asked Questions

Who can join this PySpark course?

Beginners, software developers, data analysts and aspiring data engineers can join. The course starts with Python essentials, so you do not need earlier Spark experience.

What are the prerequisites for learning Spark and PySpark?

You do not need earlier Spark, Hadoop or Python experience. The course teaches Python, Linux and Git before it moves into Spark. You should be comfortable using a computer and ready to write code. If you are unsure, speak with the learning team before you enrol.

What is the PySpark course duration?

The course runs for [Confirm duration].

What is the PySpark course fee?

The self paced course costs ₹5,000. The live online fee is [Confirm online fee] and the classroom fee is the online fee plus ₹500. Contact the learning team for EMI details.

What is the difference between PySpark and Apache Spark with Scala?

Both use the same Spark engine. PySpark uses Python and the Scala route uses Scala. This course teaches Spark through Python. Our Apache Spark Scala course teaches the Scala route.

Does the course cover Spark SQL?

Yes. The DataFrames and Spark SQL module covers DataFrames, Spark SQL architecture, temporary and global views, complex queries and user defined functions.

Will I work with Databricks and cloud platforms?

Yes. The cloud module introduces Databricks, workspaces, notebooks and clusters, AWS EMR, AWS Glue basics and Azure Databricks. It is an introduction and not exam preparation.

Do I get a PySpark certification?

You receive a Prwatech certificate of completion, and it is included in the course. It is a course certificate and not a vendor exam such as a Databricks certification. Databricks exam preparation is not part of this course.

Do you provide job assistance?

Learners who pass the screening test receive interview calls. We also help with your CV and with interview preparation. We do not guarantee a job offer.

What if I miss a live class?

Every class is recorded and uploaded to your LMS. Earlier class recordings and presentations are also available to you.

Start Learning PySpark

Get the complete curriculum and speak with a learning advisor about the right learning format for you.

Download Curriculum Book a Free Demo Class

PySpark Online Course with Certification

Who Should Take This PySpark Course?

Beginners

Software Developers

Data Analysts

Aspiring Data Engineers

Learning PySpark from Scratch: What You Will Learn

Hands On PySpark Projects

Capstone Industry Data Pipeline

ETL and Performance Tuning Practicals

Streaming, Delta Lake and Airflow Practicals

PySpark Career Roles After This Course

How Prwatech supports your job search

CV Help

Interview Preparation

Capstone Project

Screening Test

Job Assistance

Why Learn PySpark with Prwatech

PySpark Trainers

[Trainer Name]

[Trainer Name]

Student Reviews

PySpark Course Syllabus

PySpark Course Fees and Learning Formats

What happens after you enquire?

Frequently Asked Questions

Start Learning PySpark

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