Big Data Hadoop Spark Development

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Big Data Hadoop Spark Development

Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology in Big Data architecture. Big Data training is best suited for IT, data management, and analytics professionals looking to gain expertise in Big Data.

Target Audience

  • Software Developers and Architects
  • Analytics Professionals
  • Senior IT professionals
  • Testing and Mainframe Professionals
  • Data Management Professionals
  • Business Intelligence Professionals
  • Project Managers
  • Aspiring Data Scientists
  • Graduates looking to build a career in Big Data Analytics

Prerequisites

There are no prerequisites for learning this course. However, knowledge of Core Java and SQL will be beneficial, but certainly not a mandate.

Course Objectives

The Big Data Hadoop Certification course is designed to give you in-depth knowledge of the Big Data framework using Hadoop and Spark, including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion with our big data training.

You will master real-time data processing using Spark, including functional programming in Spark, implementing Spark applications, understanding parallel processing in Spark, and using Spark RDD optimization techniques. With our big data course, you will also learn the various interactive algorithms in Spark and use Spark SQL for creating, transforming, and querying data forms.

 


 

Course Curriculum


Section 1: Big Data Hadoop and Spark Developers

  • Course Introduction
  • Introduction to Big data and Hadoop Ecosystem
  • HDFS and YARN
  • Map Reduce and Sqoop
  • Basics of Hive and Impala
  • Working with Hive and Impala
  • Types of Data Formats
  • Advanced Hive Concept and Data File Partitioning
  • Apache Flume and HBase
  • Pig
  • Basics of Apache Spark
  • RDDs in Spark
  • Implementation of Spark Applications
  • Spark Parallel Processing
  • Spark Algorithm
  • Spark SQL
  • Projects