PySpark for Data Engineers Course Overview

PySpark for Data Engineers Course Overview

Unlock the power of big data with our PySpark for Data Engineers course at Koenig Solutions. This course is designed to equip you with the essential skills to efficiently process large datasets using Apache Spark and Python. You will learn how to work with dataframes, optimize data processing, and perform complex transformations, enabling you to handle real-time data challenges effectively. By the end of the course, you will confidently apply your knowledge to build scalable data pipelines and improve data analytics workflows. Join us to enhance your data engineering expertise and drive impactful decisions through data-driven insights. Dive into the world of PySpark today and elevate your career!

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1,700

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Course Fee 1,700
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1,700 (USD)
  • Live Training (Duration : 40 Hours)
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  • Live Training (Duration : 40 Hours)
  • Per Participant
  • Classroom Training fee on request

♱ Excluding VAT/GST

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

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Target Audience for PySpark for Data Engineers

PySpark for Data Engineers is an advanced course designed to equip professionals with the skills to process large datasets using Apache Spark, enhancing data engineering capabilities.


  • Data Engineers
  • Big Data Analysts
  • Data Scientists
  • Business Intelligence Developers
  • Machine Learning Engineers
  • ETL Developers
  • Software Developers shifting to Data Engineering
  • Database Administrators
  • Solutions Architects
  • IT Managers overseeing data projects


Learning Objectives - What you will Learn in this PySpark for Data Engineers?

Introduction

The PySpark for Data Engineers course is designed to equip students with essential skills in big data processing using Apache Spark, emphasizing data manipulation, transformation, and analysis with PySpark in a hands-on learning environment.

Learning Objectives and Outcomes

  • Understand the fundamentals of Apache Spark and its ecosystem.
  • Learn how to install and configure PySpark.
  • Gain proficiency in DataFrame operations for data manipulation.
  • Explore data ingestion techniques from various sources.
  • Utilize Spark SQL for querying structured data seamlessly.
  • Master data transformations using RDDs and DataFrames.
  • Implement machine learning algorithms using PySpark MLlib.
  • Optimize Spark applications for performance and efficiency.
  • Develop skills for real-time data processing with Spark Streaming.
  • Learn best practices for data engineering in big data environments.

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