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Big Data processing with Pyspark and Apache Airflow Course Overview

Big Data processing with Pyspark and Apache Airflow Course Overview

Discover the power of Big Data processing with our comprehensive course on PySpark and Apache Airflow. This course is designed to equip you with essential skills in data manipulation and workflow orchestration, ideal for handling large datasets efficiently.

Through engaging lessons, you will learn to effectively use PySpark for distributed data processing and Apache Airflow for creating robust data pipelines. By the end of the course, you’ll not only understand these technologies but also be able to apply them in real-world scenarios, enhancing your ability to work with big data projects. Join us to elevate your career in the growing field of data engineering!

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Course Fee 1,150
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1,150 (USD)
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  • Live Training (Duration : 24 Hours)
  • Per Participant
  • Classroom Training fee on request
Koeing Learning Stack

Koenig Learning Stack

Free Pre-requisite Training

Join a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.

Assessments (Qubits)

Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.

Post Training Reports

Receive comprehensive post-training reports summarizing your performance. These reports offer clear feedback and recommendations to help you confidently take the next steps in your learning journey.

Class Recordings

Get access to class recordings anytime. These recordings let you revisit key concepts and ensure you never miss important details, supporting your learning even after class ends.

Free Lab Extensions

Extend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.

Free Revision Classes

Join our free revision classes to reinforce your learning. These classes revisit important topics, clarify doubts, and help solidify your understanding for better training outcomes.

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

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♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

Request More Information

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Target Audience for Big Data processing with Pyspark and Apache Airflow

Big Data processing with PySpark and Apache Airflow equips professionals with essential skills to manage and analyze vast datasets effectively, making it ideal for data-driven roles.


  • Data Engineers
  • Data Scientists
  • Big Data Analysts
  • Machine Learning Engineers
  • Business Intelligence Developers
  • Software Developers
  • Data Architects
  • IT Project Managers
  • DevOps Engineers
  • Analytics Consultants
  • Research Analysts
  • Database Administrators
  • Cloud Engineers
  • System Administrators
  • Technical Trainers


Learning Objectives - What you will Learn in this Big Data processing with Pyspark and Apache Airflow?

Course Overview

The Big Data Processing with PySpark and Apache Airflow course equips students with essential skills to manage and analyze large datasets efficiently while utilizing Apache Airflow for workflow orchestration.

Learning Objectives and Outcomes

  • Understand the fundamentals of Big Data and its importance in the modern data landscape.
  • Gain proficiency in using PySpark for data processing and transformation.
  • Explore advanced data manipulation techniques with DataFrames and RDDs in PySpark.
  • Learn to deploy and manage Apache Airflow for orchestrating data workflows.
  • Implement scheduling and monitoring for data pipelines using Apache Airflow.
  • Develop hands-on experience in building and executing complex data processing tasks.
  • Understand best practices for optimizing PySpark applications for performance.
  • Discover methods for scaling data processing tasks across distributed systems.
  • Learn to integrate PySpark with various data sources and sinks.
  • Acquire skills in troubleshooting and debugging data processing workflows in a Big Data environment.

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