PySpark online course
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    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

    or call +91 8147111254. Monday to Saturday, 9 AM to 7 PM.

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      Course experience level:


      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.
      Find My Course →

      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:
      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
      Verify against internal placement records and delete this line before the page goes live.
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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

      Photo

      [Trainer Name]

      [Current role]
      [Years of experience]
      Specialization: [Specialization]
      Photo

      [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.

      Read All Reviews on Google →
      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.

      Call our Counselors ✆+91 8147111254