Python, Spark, and Hadoop for Big Data Training Course


21 hours (usually 3 days including breaks)


  • Experience with Spark and Hadoop
  • Python programming experience


  • Data scientists
  • Developers


Python is a scalable, flexible, and widely used programming language for data science and machine learning. Spark is a data processing engine used in querying, analyzing, and transforming big data, while Hadoop is a software library framework for large-scale data storage and processing.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.

By the end of this training, participants will be able to:

  • Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
  • Understand the features, core components, and architecture of Spark and Hadoop.
  • Learn how to integrate Spark, Hadoop, and Python for big data processing.
  • Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
  • Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
  • Use Apache Mahout to scale machine learning algorithms.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

Course Outline


  • Overview of Spark and Hadoop features and architecture
  • Understanding big data
  • Python programming basics

Getting Started

  • Setting up Python, Spark, and Hadoop
  • Understanding data structures in Python
  • Understanding PySpark API
  • Understanding HDFS and MapReduce

Integrating Spark and Hadoop with Python

  • Implementing Spark RDD in Python
  • Processing data using MapReduce
  • Creating distributed datasets in HDFS

Machine Learning with Spark MLlib

Processing Big Data with Spark Streaming

Working with Recommender Systems

Working with Kafka, Sqoop, Kafka, and Flume

Apache Mahout with Spark and Hadoop


Summary and Next Steps

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