Big Data Hadoop Developer Training Course Online Course

Big Data Hadoop Developer Training Course

All levels
English

Course Description

Big Data Hadoop Developer Professional Program delivers the key concepts and expertise necessary to develop robust data processing applications using Apache Hadoop. The interactive sessions and demonstrations carried by an industry expert will help the aspirants in understanding all the features and programming skills easily. The Hadoop developer course focuses on the fundamentals and advanced topics of Hadoop, MapReduce, Hadoop Distributed File System (HDFC), Hadoop cluster, Pig, Hive, Hbase, ZooKeeper, Sqoop, and Flume. Big Data Analytics takes into account exabytes and petabytes of data and provides solutions to deal with the rapid flow of such huge amounts of data. BIT’s Hadoop developer training will help you master complete Hadoop development. You will trained in the domains of HDFS, MapReduce, working with various components of Hadoop like Pig, Hive, Sqoop, YARN and others.

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What you’ll learn

Live Class Practical Oriented Training
Timely Doubt Resolution
Dedicated Student Success Mentor
Certification & Job Assistance
Free Access to Workshop & Webinar
No Cost EMI Option
Fundamentals of Hadoop and YARN and write applications using them
Spark, Spark SQL, Streaming, Data Frame, RDD, GraphX and MLlib writing Spark applications
Set up different configurations of Hadoop cluster
Leverage Pig, Hive, Hbase, ZooKeeper, Sqoop, Flume, and other projects from the Apache Hadoop ecosystem
Practicing real-life projects using Hadoop and Apache Spark
Setting up pseudo-node and multi-node clusters on Amazon EC2
Hadoop administration activities like cluster managing, monitoring, administration and troubleshooting
Maintain and monitor Hadoop cluster by considering the optimal hardware and networking settings
Hadoop testing applications using MRUnit and other automation tools

Covering Topics

Lecture-1 Introduction to Apache Hadoop and the Hadoop Ecosystem

Lecture-2 Apache Hadoop File Storage

Lecture-3 Distributed Processing on an Apache Hadoop Cluster

Lecture-4 Apache Spark Basics

Lecture-5 Working with DataFrames and Schemas

Lecture-6 Analyzing Data with DataFrame Queries

Lecture-7 RDD Overview

Lecture-8 Transforming Data with RDDS

Lecture-9 Aggregating Data with Pair RDDS

Lecture-10 Querying Tables and Views with SQL

Lecture-11 Working with Datasets in Scala

Lecture-12 Writing, Configuring, and Running Spark Applications

Lecture-13 Spark Distributed Processing

Lecture-14 Distributed Data Persistence

Lecture-15 Common Patterns in Spark Data Processing

Lecture-16 Introduction to Structured Streaming

Lecture-17 Structured Streaming with Apache Kafka

Lecture-18 Aggregating and Joining Streaming DataFrames

Lecture-19 Message Processing with Apache Kafka

Curriculum

  Lecture 1 Introduction to Apache Hadoop and the Hadoop Ecosystem
·       Apache Hadoop Overview
·       Data Processing
·       Introduction to the Hands-On Exercises
·       Practical Exercise

  Lecture-2 Apache Hadoop File Storage

·       Apache Hadoop Cluster Components
·       HDFS Architecture
·       Using HDFS
·       Practical Exercise

  Lecture-3 Distributed Processing on an Apache Hadoop Cluster
·       YARN Architecture
·       Working With YARN
·       Practical Exercise

  Lecture-4 Apache Spark Basics
·       What is Apache Spark?

·       Starting the Spark Shell

·       Using the Spark Shell

·       Getting Started with Datasets and DataFrames

·       DataFrame Operations

·       Practical Exercise

  Lecture-5 Working with DataFrames and Schemas
·       Creating DataFrames from Data Sources

·       Saving DataFrames to Data Sources

·       DataFrame Schemas

·       Eager and Lazy Execution

·       Practical Exercise

  Lecture-6 Analyzing Data with DataFrame Queries
·       Querying DataFrames Using Column Expressions

·       Grouping and Aggregation Queries

·       Joining DataFrames

·       Practical Exercise

  Lecture-7 RDD Overview
·       RDD Overview

·       RDD Data Sources

·       Creating and Saving RDDs

·       RDD Operations

·       Practical Exercise

  Lecture-8 Transforming Data with RDDs
·       Writing and Passing Transformation Functions

·       Transformation Execution

·       Converting Between RDDs and DataFrames

·       Practical Exercise

  Lecture-9 Aggregating Data with Pair RDDs
·       Key-Value Pair RDDs

·       Map-Reduce

·       Other Pair RDD Operations

·       Practical Exercise

  Lecture-10 Querying Tables and Views with SQL
·       Querying Tables in Spark Using SQL

·       Querying Files and Views

·       The Catalog API

·       Practical Exercise

  Lecture-11 Working with Datasets in Scala
·       Datasets and DataFrames

·       Creating Datasets

·       Loading and Saving Datasets

·       Dataset Operations

·       Practical Exercise

  Lecture-12 Writing, Configuring, and Running Spark Applications
·       Writing a Spark Application

·       Building and Running an Application

·       Application Deployment Mode

·       The Spark Application Web UI

·       Configuring Application Properties

·       Practical Exercise

  Lecture-13 Spark Distributed Processing
·       Review: Apache Spark on a Cluster

·       RDD Partitions

·       Example: Partitioning in Queries

·       Stages and Tasks

·       Job Execution Planning

·       Example: Catalyst Execution Plan

·       Example: RDD Execution Plan

·       Practical Exercise

  Lecture-14 Distributed Data Persistence
·       DataFrame and Dataset Persistence

·       Persistence Storage Levels

·       Viewing Persisted RDDs

·       Practical Exercise

  Lecture-15 Common Patterns in Spark Data Processing
·       Common Apache Spark Use Cases

·       Iterative Algorithms in Apache Spark

·       Machine Learning

·       Example: k-means

·       Practical Exercise

  Lecture-16 Introduction to Structured Streaming
·       Apache Spark Streaming Overview

·       Creating Streaming DataFrames

·       Transforming DataFrames

·       Executing Streaming Queries

·       Practical Exercise

  Lecture-17 Structured Streaming with Apache Kafka
·       Overview

·       Receiving Kafka Messages

·       Sending Kafka Messages

·       Practical Exercise

  Lecture-18 Aggregating and Joining Streaming DataFrames
·       Streaming Aggregation

·       Joining Streaming DataFrames

·       Conclusion

·       Practical Exercise

  Lecture-19 Message Processing with Apache Kafka
·       What Is Apache Kafka?

·       Apache Kafka Overview

·       Scaling Apache Kafka

·       Apache Kafka Cluster Architecture

·       Apache Kafka Command Line Tools

·       Practical Exercise

Frequently Asked Questions

You don’t need prior knowledge of Apache Hadoop.

The course offers a variety of online training options, including: • Live Virtual Classroom Training: Participate in real-time interactive sessions with instructors and peers. • 1:1 Doubt Resolution Sessions: Get personalized assistance and clarification on course-related queries. • Recorded Live Lectures*: Access recorded sessions for review or to catch up on missed classes. • Flexible Schedule: Enjoy the flexibility to learn at your own pace and according to your schedule.

Live Virtual Classroom Training allows you to attend instructor-led sessions in real-time through an online platform. You can interact with the instructor, ask questions, participate in discussions, and collaborate with fellow learners, simulating the experience of a traditional classroom setting from the comfort of your own space.

If you miss a live session, you can access recorded lectures* to review the content covered during the session. This allows you to catch up on any missed material at your own pace and ensures that you don't fall behind in your learning journey.

The course offers a flexible schedule, allowing you to learn at times that suit you best. Whether you have other commitments or prefer to study during specific hours, the course structure accommodates your needs, enabling you to balance your learning with other responsibilities effectively.