Hadoop Training

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HKR Trainings provides the best and industry oriented hadoop training to gain skills in order to build your professional career . Our course curriculum covers all the concepts to gain real-time proficiency in core essentials of hadoop, hadoop fundamentals, you will be mastering in the basic to advanced level concepts, etc.Moreov.....er with comprehensive training helps to gain expertise in this comprehensive field so as to accomplish your daily tasks for ease. Our expert trainers deliver the lecturers in a more practical way so as to gain realistic experience. Get the industry-oriented hadoop training course from the certified mentors by joining HKR. Read more

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Why should I learn Hadoop?

There is a great future scope and career growth for the one who opts this techno.....logy. Read more

Certification in hadoop will show a wide range of opportunities for the job seek.....ers as well as for professionals. Read more

Moreover, it has a demanding profile and most companies are searching for the ce.....rtified professionals in this field. Read more

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Hadoop Course Overview

Apache Hadoop is an open source platform that is used to efficiently store and process massive datasets ranging in size from gigabytes to petabytes. Instead of using a single computer to store and process data, Hadoop allows many computer clusters to analyse huge datasets more rapidly in parallel.

HKR delivers the best industry-oriented hadoop training course that is in line to clear the certification exams. Our course covers all the key concepts such as key fundamentals of hadoop, hadoop concepts, building blocks of hadoop, apache hive, flume, streaming, big data hadoop optimizations, etc. During the training period, you can get full support and real-time project assistance from experienced professionals. Enroll today at HKR for accepting the new challenges to make the best out of our hadoop online training.

Hadoop Course Content Download Curriculum

The Hadoop course curriculum is structured to streamline the learning process by a team of experts. You can find the complete course details in below-mentioned modules

1.1 Introduction to Big Data and Hadoop

1.2 Introduction to Big Data

1.3 Big Data Analytics

1.4 What is Big Data

1.5 Four Vs Of Big Data

1.6 Case Study Royal Bank of Scotland

1.7 Challenges of Traditional System

1.8 Distributed Systems

1.9 Introduction to Hadoop

1.10 Components of Hadoop Ecosystem 

1.11 Commercial Hadoop Distributions

2.1 Introduction to Hadoop Architecture Distributed Storage (HDFS) and YARN

2.2 What Is HDFS

2.3 Need for HDFS

2.4 Regular File System vs HDFS

2.5 Characteristics of HDFS

2.6 HDFS Architecture and Components

2.7 High Availability Cluster Implementations

2.8 HDFS Component File System Namespace

2.9 Data Block Split

2.10 Data Replication Topology

2.11 HDFS Command Line

2.12 YARN Introduction

2.13 YARN Use Case

2.14 YARN and Its Architecture

2.15 Resource Manager

2.16 How Resource Manager Operates

2.17 Application Master

2.18 How YARN Runs an Application

2.19 Tools for YARN Developers

3.1 Introduction to Data Ingestion into Big Data Systems and ETL

3.2 Overview of Data Ingestion

3.3 Apache Sqoop

3.4 Sqoop and Its Uses

3.5 Sqoop Processing

3.6 Sqoop Import Process

3.7 Sqoop Connectors

3.8 Apache Flume

3.9 Flume Model

3.10 Scalability in Flume

3.11 Components in Flume’s Architecture

3.12 Configuring Flume Components

3.13 Apache Kafka

3.14 Aggregating User Activity Using Kafka

3.15 Kafka Data Model

3.16 Partitions

3.17 Apache Kafka Architecture

3.18 Producer Side API Example

3.19 Consumer Side API

3.20 Consumer Side API Example

3.21 Kafka Connect

4.1 Introduction to Distributed Processing MapReduce Framework and Pig

4.2 Distributed Processing in MapReduce

4.3 Word Count Example

4.4 Map Execution Phases

4.5 Map Execution Distributed Two Node Environment

4.6 MapReduce Jobs

4.7 Hadoop MapReduce Job Work Interaction

4.8 Setting Up the Environment for MapReduce Development

4.9 Set of Classes

4.10 Creating a New Project

4.11 Advanced MapReduce

4.12 Data Types in Hadoop

4.13 OutputFormats in MapReduce

4.14 Using Distributed Cache

4.15 Joins in MapReduce

4.16 Replicated Join

4.17 Introduction to Pig

4.18 Components of Pig

4.19 Pig Data Model

4.20 Pig Interactive Modes

4.21 Pig Operations

4.22 Various Relations Performed by Developers

5.1 Introduction to Apache Hive

5.2 Hive SQL over Hadoop MapReduce

5.3 Hive Architecture

5.4 Interfaces to Run Hive Queries

5.5 Running Beeline from Command Line

5.6 Hive Metastore

5.7 Hive DDL and DML

5.8 Creating New Table

5.9 Data Types

5.10 Validation of Data

5.11 File Format Types

5.12 Data Serialization

5.13 Hive Table and Avro Schema

5.14 Hive Optimization Partitioning Bucketing and Sampling

5.15 Non-Partitioned Table

5.16 Data Insertion

5.17 Dynamic Partitioning in Hive

5.18 Bucketing

5.19 What Do Buckets Do

5.20 Hive Analytics UDF and UDAF

5.21 Other Functions of Hive

6.1 Introduction to NoSQL Databases HBase

6.2 NoSQL Introduction

6.3 HBase Overview

6.4 HBase Architecture

6.5 Data Model

6.6 Connecting to HBase

7.1 Introduction to the basics of Functional Programming and Scala

7.2 Introduction to Scala

7.3 Functional Programming

7.4 Programming with Scala

7.5 Type Inference Classes Objects and Functions in Scala

7.6 Collections

7.7 Types of Collections

7.8 Scala REPL

8.1 Introduction to Apache Spark Next-Generation Big Data Framework

8.2 History of Spark

8.3 Limitations of MapReduce in Hadoop

8.4 Introduction to Apache Spark

8.5 Components of Spark

8.6 Application of In-Memory Processing

8.7 Hadoop Ecosystem vs Spark

8.8 Advantages of Spark

8.9 Spark Architecture

8.10 Spark Cluster in Real World

9.1 Processing RDD

9.2 Introduction to Spark RDD

9.3 RDD in Spark

9.4 Creating Spark RDD

9.5 Pair RDD

9.6 RDD Operations

9.7 Demo: Spark Transformation Detailed Exploration Using Scala Examples

9.8 Demo: Spark Action Detailed Exploration Using Scala

9.9 Caching and Persistence

9.10 Storage Levels

9.11 Lineage and DAG

9.12 Need for DAG

9.13 Debugging in Spark

9.14 Partitioning in Spark

9.15 Scheduling in Spark

9.16 Shuffling in Spark

9.17 Sort Shuffle

9.18 Aggregating Data with Pair RDD

10.1 Introduction to Spark SQL Processing DataFrames

10.2 Spark SQL Introduction

10.3 Spark SQL Architecture

10.4 DataFrames

10.5 Demo: Handling Various Data Formats

10.6 Demo: Implement Various DataFrame Operations

10.7 Demo: UDF and UDAF

10.8 Interoperating with RDDs

10.9 Demo: Process DataFrame Using SQL Query

10.10 RDD vs DataFrame vs Dataset

11.1 Introduction to Spark MLlib Modeling Big Data with Spark

11.2 Role of Data Scientist and Data Analyst in Big Data

11.3 Analytics in Spark

11.4 Machine Learning

11.5 Supervised Learning

11.6 Demo: Classification of Linear SVM

11.7 Demo: Linear Regression with Real-World Case Studies

11.8 Unsupervised Learning

11.9 Demo: Unsupervised Clustering K-Means

11.10 Reinforcement Learning

11.11 Semi-Supervised Learning

11.12 Overview of MLlib

11.13 MLlib Pipelines

12.1 Introduction to Stream Processing Frameworks and Spark Streaming

12.2 Overview of Streaming 

12.3 Real-Time Processing of Big Data

12.4 Data Processing Architectures

12.5 Spark Streaming

12.6 Introduction to DStreams

12.7 Transformations on DStreams

12.8 Design Patterns for Using ForeachRDD

12.9 State Operations

12.10 Windowing Operations

12.11 Join Operations stream-dataset Join

12.12 Streaming Sources

12.13 Structured Spark Streaming

12.14 Use Case Banking Transactions

12.15 Structured Streaming Architecture Model and Its Components

12.16 Output Sinks

12.17 Structured Streaming APIs

12.18 Constructing Columns in Structured Streaming

12.19 Windowed Operations on Event-Time

12.20 Use Cases

13.1 Introduction to Spark GraphX

13.2 Introduction to Graph

13.3 Graphx in Spark

13.4 Graph Operators

13.5 Join Operators

13.6 Graph Parallel System

13.7 Algorithms in Spark

13.8 Pregel API

13.9 Use Case of GraphX

We at HKR trainings provide the learners with practice Mock Interview Sessions and excellent Job Support at the end of the course.
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Customised course creation

Hadoop Training Advantages

This Technology Offers Excellent Career Opportunities Worldwide.

Salaries Offered for Certified Professionals is Very High and More Number of People Started Learning this Course.

It has a Great Learning Scope

Streamlined Work Process Helps You Execute all Complex Tasks Easily.

Fast track your career growth with Hadoop Training Certification course.

Hadoop Online Training Objectives

The Hadoop training course benefits for the following list of professionals.

  • Programming Developers and System Administrators.
  • Experienced working professionals and Project Managers.
  • Big Data Hadoop Developers eager to learn other verticals like testing, analytics and administration.
  • Mainframe Professionals, Architects and Testing Professionals.
  • Business Intelligence, Data Warehousing and Analytics Professionals.
  • Graduates and undergraduates eager to learn Big Data.

Aspirants should have a basic understanding of Core Java and SQL. 

To start with the Hadoop training course, you need to check with the best institute that delivers knowledge. Before proceeding to join any training, take suggestions from the experts who had already learned the course. We at HKR, with a team of industry experts, are ready to fulfil your dream career to achieve a job in desired companies.

Once you complete the entire course along with real-time projects and assignments, HKR delivers the course completion certification. This certification helps to get a job in any company very quickly.

Our trainers are highly qualified and certified with many years of industry experience with a technical background in Hadoop.

Certification differentiates you from the non-certified peers, and you can demand the best salary in the leading companies.

We, at HKR, provide complete guidance to reach your dream job. But your job will be based on your performance in the interview panel and the recruiter requirements.

Interested in our Hadoop Training program ?

Hadoop Training Options

We follow four Hadoop Training formats for the flexibility of our students.

Live Online Training

  • » Interact live with industrial experts.
  • » Flexible Schedule.
  • » Free Demo before Enroll.
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1:1 Live Online Training

  • » Dedicated Trainer for you.
  • » 1:1 Total Online Training.
  • » Customizable Curriculum.
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  Contact Us

Self-Paced E-Learning

  • » Get E-Learning Videos.
  • » Learn Whenever & Wherever.
  • » Lifetime free Upgrade.
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Corporate Training

  • » Customized Training.
  • » Live Online/Classroom/Self-paced.
  • » 10+ years Industrial Expert Trainers.

Certification

Certifications play a vital role in showcasing your tech skills and also help to achieve your dream career. Moreover, it shows your talent to connect with real-time projects. Many companies are preferring the candidates with certifications as their main priority and also provide substantial salary packages.

Cloudera Certified Administrator for Apache Hadoop (CCAH)

The certification tests the aspirant’s skills and knowledge in the following topics of HDFS, YARN, Hadoop Cluster planning, Hadoop cluster installation and administration, resource management and Monitoring and logging.

  • Exam Title: Cloudera Certified Administrator for Apache Hadoop (CCAH)
  • Duration: 90 minutes
  • Number of Questions: 60
  • Passing score: 70%

We at HKR, help the candidates to achieve certifications through our industry-oriented training. Once, you're done with complete training along with real-time projects then we provide the course completion certificate. It illustrates that you are proficient in the technology to handle various real-time projects. HKR certification is well recognized across all top MNCs and helps you get into a job quickly.

HKR Trainings Certification

Interested in our Hadoop Training program ?

Hadoop Projects

Every course we offer includes real-time projects which provide you with real-time experience. The practical knowledge improves your domain expertise and helps you in clearing the certifications with ease.

Use Hadoop features to predict patterns and share actionable insights for a car insurance company.

Use Hive features for data engineering and analysis of New York stock exchange data.

Perform sentiment analysis on employee review data gathered from Google, Netflix, and Facebook.

Perform product and customer segmentation to increase the sales of Amazon.

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Our Learners

Pavani

Pavani

Awesome journey with HKR trainings on the hadoop training as the industry certified delivered the co.....urse. Covered all the topics in depth, 100% doubt clarification sessions, real time project assistance. Thanks. Read more

Suraj

Suraj

If you want to build your career in hadoop then I definitely recommend you the HKR trainings to take..... the hadoop training. Because all the trainers are very proficient and experienced in the technology topics and delivered good course lectures to the attendees by covering all the concepts in depth. Thanks once again to the entire team at HKR trainings for their efforts to lighten our career. Read more

Balu

Balu

Superb training institute on hadoop training. All the concepts are covered with neat flow from basic..... to advanced levels. The trainers are well proficient in dealing with the attendees. All the course structure was designed perfectly and completed on time. Thanks. Read more

Hadoop Training Reviews

FAQ's

Each and every class is recorded so if you missed any class you can review the recordings and clarify any doubts with the trainer in next class.   

Yes, we don’t assure 100% placement assistance. We are tied up with some corporate companies so when they have a requirement we send your profiles to them.

Yes, we provide demo before starting any training in which you can clear all your doubts before starting training.

Our trainers are real-time experts who are presently working on a particular platform on which they are providing training.

You can call our customer care 24/7

Max of the students gets satisfied with our training if you are not then we provide specialised training in return.

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