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Hadoop Developer with Spark

The Hadoop Developer with Spark course provides a comprehensive learning experience for developers eager to master big data processing with Apache Hadoop and Apache Spark. This hands-on training is designed to help you understand the core concepts of distributed computing, big data frameworks, and data processing using Hadoop and Spark. You’ll gain practical knowledge on how to leverage these technologies for large-scale data storage, management, and processing. By the end of the course, you'll be equipped to develop and implement big data applications that scale seamlessly on the Hadoop ecosystem and Spark platform.

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Schedule Learners Course Fee (Incl. of all Taxes) Register Your Interest
March 02nd - 06th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
20% Off
$2,000
$1,600
Fast Filling! Hurry Up.
March 07th - 21st
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
20% Off
$2,000
$1,600
March 09th - 13th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
20% Off
$2,000
$1,600
March 16th - 27th
06:00 AM - 10:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
20% Off
$2,000
$1,600
March 16th - 20th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
Guaranteed-to-Run
20% Off
$2,000
$1,600
March 22nd - 05th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
20% Off
$2,000
$1,600
March 23rd - 27th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
Guaranteed-to-Run
20% Off
$2,000
$1,600
April 06th - 10th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
25% Off
$2,000
$1,500
April 11th - 25th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
25% Off
$2,000
$1,500
April 13th - 17th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
25% Off
$2,000
$1,500
April 20th - 01st
06:00 AM - 10:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
25% Off
$2,000
$1,500

Course Prerequisites

  • Basic programming skills, preferably in Java or Scala
  • Familiarity with SQL and relational databases
  • Basic understanding of Linux and command-line operations
  • Fundamental knowledge of big data concepts is beneficial
  • Prior experience with Hadoop or Apache Spark is recommended but not mandatory

Learning Objectives

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

  • Understand the architecture and components of Hadoop and Apache Spark
  • Set up and configure Hadoop and Spark clusters
  • Build Spark applications using RDDs, DataFrames, and Spark SQL
  • Process real-time data with Spark Streaming and integrate it with other systems
  • Optimize the performance of Spark jobs and data pipelines
  • Deploy and manage big data applications on Hadoop and Spark clusters
  • Implement machine learning and graph processing with Spark MLlib and GraphX
  • Work with the Hadoop ecosystem, including Hive, HBase, and Sqoop

Target Audience

This course is designed for developers, data engineers, and professionals interested in learning how to develop, process, and manage big data applications using Hadoop and Apache Spark. The target audience includes:

  • Hadoop Developers
  • Data Engineers
  • Spark Developers
  • Big Data Professionals
  • Software Developers looking to work with big data processing tools
  • IT professionals interested in building big data solutions with Apache Hadoop and Apache Spark

Course Modules

  1. Introduction to Hadoop and Spark

    • Overview of Hadoop and Apache Spark and their role in big data processing
    • Hadoop architecture, components, and its ecosystem (HDFS, MapReduce, YARN)
    • Spark architecture and its integration with Hadoop
    • Difference between Hadoop MapReduce and Spark processing models

  2. Setting Up Hadoop and Spark Environments

    • Installing and configuring Hadoop and Spark
    • Understanding the Hadoop Distributed File System (HDFS)
    • Setting up and managing a Spark cluster on YARN or Mesos
    • Working with Spark shell and interactive analysis

  3. Data Processing with Hadoop and Spark

    • Understanding data processing workflows in Hadoop (MapReduce) vs. Spark (RDDs, DataFrames, Datasets)
    • Writing Hadoop jobs with MapReduce and Spark applications
    • Leveraging Spark SQL and DataFrames for structured data processing
    • Working with Spark Streaming for real-time data processing

  4. Spark Core Concepts

    • Understanding RDDs (Resilient Distributed Datasets) and Transformations
    • Using Actions, Caching, and Persisting for performance optimization
    • Implementing Spark SQL for querying data with Hive and Parquet
    • Advanced operations like Joins, Aggregations, and GroupBy

  5. Integrating Hadoop with Spark

    • Connecting Hadoop ecosystem tools like Hive, HBase, and Sqoop with Spark
    • Using Spark with HDFS for efficient data storage and processing
    • Integrating with Apache Kafka for data streaming and ingestion
    • Best practices for data loading and data writing in Hadoop/Spark ecosystems

  6. Performance Tuning and Optimization

    • Understanding Spark performance and tuning concepts
    • Optimizing RDDs, DataFrames, and Spark jobs for improved performance
    • Memory management and garbage collection strategies
    • Efficient use of Spark’s Catalyst optimizer and Tungsten execution engine

  7. Real-time Data Processing with Spark Streaming

    • Introduction to Spark Streaming for real-time data processing
    • Working with DStreams and structured streaming in Spark
    • Implementing real-time data pipelines with Spark Streaming
    • Integrating with Kafka, Flume, and Kinesis for real-time data ingestion

  8. Advanced Hadoop and Spark Topics

    • Implementing machine learning with MLlib in Spark
    • Using GraphX for graph processing with Spark
    • Data lineage, versioning, and audit tracking in big data applications
    • Security in the Hadoop ecosystem (Kerberos, HDFS encryption)

  9. Deploying Big Data Applications on Hadoop and Spark

    • Deploying Spark jobs to the cluster using YARN
    • Running and monitoring Hadoop MapReduce and Spark applications on the cluster
    • Troubleshooting and debugging Spark and Hadoop applications
    • Managing data pipelines and automating workflows with Apache Airflow

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What Our Learners Are Saying

The training, courseware, and lab experience were insightful and valuable. Keep up the great work and learning experience!

Nitish A. Anand – Accenture

Course: SC-200: Microsoft Security Operations Analyst
Date: 15th Jan 2025

The instructor was professional and very content.

Justine Daudi Mlimbilah – Bank of Africa, Tanzania

Course: MD-102: Microsoft 365 Endpoint Administrator
Date: 20th Dec 2024

The instructor was so knowledgeable & humble. Rare to find someone so confident but so down to earth these days. So appreciative to him.”

Mohd. Hassan – Ministry of Finance, UAE

Course: AZ-700: Designing and Implementing Microsoft Azure Networking Solutions
Date: 31st July 2024

Instructor is experienced and knowledgeable in guiding.

Dharshini Mahalaxmi – Dr. MGR Education and Research Institute, Chennai, India

Course: SC-300: Microsoft Identity and Access Administrator
Date: 4th May 2024