Big Data Hadoop Development Training Course

Magnify your career in Big Data Hadoop and become a Data Scientist.

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Course
Duration

60

hours

Advantages of Big Data Hadoop Course

₹ 2,50,000+

Average Salary per Annum in India

$ 50,000

Average Salary per Annum in USA

15,00,000

Job Vacancies across the world

1,00,000

Big Data Hadoop Developers in the world and the demand is increasing day by day

Why Choose Big Data Hadoop Certification Program?

As the world is growing Digital, which leads us to large datasets called Big data and for processing and storing this large datasets is a new challenge. For that, one should have skills to analysis the data so there is growing trend for Big data analytics and Hadoop professional who have a good understanding of structured, unstructured, complex data and have skills to use Hadoop Technology for storing and processing Big Data.

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Hiring Partners

We are globally connected with the top IT companies

We are connected with companies with different industries across the nation. Our dedicated placement cell is constantly striving to get more companies on-board

About Big Data Hadoop

Hadoop is an open source, smooth and easy-to-use Apache tool designed to store data, runs application on clusters and is been written in JAVA. Big Data is collection of voluminous and complex data sets that cannot be processed using traditional computer technologies. In this course we learn the Hadoop ecosystem components such as HDFS, Pig, Map reduce, yarn, impala, Hbase, Apache spark etc. which helps in Big Data processing.

Big Data Hadoop Development Course Overview

Tracks Regular Track Full Day(Fast Track)
Training Duration 60 hours 60 hours
Training Days 30 days 7 days
  • About MapReduce
  • Why MapReduce?
  • History of MapReduce
  • MapReduce Use Cases
  • Work Flow of MapReduce
  • Traditional way Vs MapReduce way to analyze BigData
  • Hadoop 2.x MapReduce Architecture
  • Hadoop 2.x MapReduce Components
  • MapReduce components
  •    â€¢Combiner
  •    â€¢Partitioner
  •    â€¢Reducer
  • Work Flow of YARN framework
  • Relation between Input Splits and HDFS Blocks
  • MapReduce Practical and Troubleshooting
  • About Hive
  • History of Hive
  • Use of Hive
  • Hive Use Case
  • Hive Vs Pig
  • Hive Architecture and Components
  • Metastore in Hive
  • Limitations of Hive
  • Traditional Database Vs Hive
  • Hive Data Types and Data Models
  • Hive Management
  • Partitions and Buckets
  •     •Hive Tables(Managed Tables and External Tables)
  •     •Importing Data
  •     •Querying Data
  •     •Managing Outputs
  •     •Hive Script
  • HiveQL
  • •Joining Tables
  • •Dynamic Partitioning
  • •Custom Map/Reduce Scripts
  • •Hive Indexes and views Hive query optimizers
  • •Hive : User Defined Functions
  • Hive Practical and Troubleshooting
  • About Sqoop
  • History of Sqoop
  • Usage and Management of sqoop with RDBMS
  • Sqoop Architecture
  • Sqoop Commands
  •    â€¢Command to get data from RDBMS form HDFS
  •    â€¢Command to put data in RDBMS form HDFS
  • Importance of sqoop with HDFS and RDBMS
  • Sqoop Practical and Troubleshooting
  • About Apache Spark
  • History of Spark and Spark Versions/Releases
  • Spark Architecture
  • Spark Components
  • Usage and Management of Spark with HDFS
  • Spark Practical
  • Spark Streaming
  • Spark MLlib
  • About Flume
  • History of Flume
  • Flume Architecture
  • Flume Components
  • Usage and Management of Flume
  • Data Fetching from many resources in HDFS using Flume
  • Flume Practical and Troubleshooting
  • About Oozie
  • History of Oozie
  • Oozie Architecture
  • Oozie Components
  • Oozie Work Flow
  • Scheduling with Oozie
  • Oozie with Hive, HBase, Pig, Sqoop, Flume
  • Oozie Practical and Troubleshooting
  • About  Zoopkeeper
  • History of Zoopkeeper
  • Zoopkeeper components
  • Zoopkeeper Architecture
  • Usage and Importance Zoopkeeper with Hadoop
  • Management of Zoopkeeper
  • Zoopkeeper Practical and Troubleshooting
  • About Cloudera Manager
  • History of Cloudera Manager
  • Usage and Management of Cloudera Manager
  • Usage and Management of each ecosystem tool with Cloudera manager.
  • Introduction and Configuration 
  • Producer API
  • Consumer API
  • Stream API
  • Connector API
  • Topics and Logs
  • Consumers and Producers
  • Kafka as messaging system
  • Kafka as a storage System
  • Kafka for Stream Processing
  • EC2 
  • EMR
  • RDS & Redshift
  • Lambda 
  • S3 storage 
  • Elastic Search 
  • Data Bricks (Azure

Project 1:
Deploying Hadoop multi node cluster and deploying application and integrated for managing big data challenge.

Technology used : Redhat Linux, Apache Hadoop, Cluster management with backend storage, python programming, Mysql BackEnd Database.

Project 2:
Deployment of Apache hadoop cluster and Managing Distributed application and Jobs scheduling  in Automation.

Technology used: Redhat Linux, Apache Hadoop, hive, pig, Sqoop, flume, Python programming, shell script

Big Data Engineer::

 This is the best job in Hadoop, big Data Engineer develop, maintain, test and evaluate big data solutions with in organisations. He builds large scale data processing systems

Hadoop Developer:

Hadoop developers are basically software programmers but working in the Big data Hadoop domain. They are masters of computer procedural languages. 

Technical Manager:

 Technical managers work with the departmental managers to ensure their team’s technological developments align with the company's goals.They are also known as information systems (CIS) managers

Lead data Engineer:

  A lead data engineer, will lead a team to architect a big data platform that is real time, stable and scalable to support data analytics, reporting data. 

Hadoop Administrator:

 hadoop administrator is responsible for ongoing administration of hadoop infrastructure, Aligning with the systems engineering team to propose and deploy new hardware and software environment required for Hadoop and to expand existing environments. 

  • Placement Assistance
  • Live Project Assessment
  • Lifetime Career Support
  • Lifetime Training Membership (Candidate can join same course again for purpose of revision and update at free of cost at our any center in India or you can solve your query by online help)
  • Hadoop Based Exam Scenario Preparation Included IN Training

Enrollment For Big Data Hadoop Development Course

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Our Team

Mr Akhilesh Jain G

Akhilesh Jain

10+ Years Experience B.Tech, AWS-CSA, RHCE & RHCSS Expertise in Network Security & AWS

Mr. Rajat Goyal

Rajat Goyal

10+ Years Experience B.Tech, RHCE, RHCSS, Cloud Certified Expertise in Linux, Cloud & Scripting

Ravi Sarswat

10+ Years Experience MCA, RHCE, RHCVAExpertise in Linux & Virtualization

Mr . Gaurav Saluja

Gaurav Saluja

10+ Years Experience B.Tech, RHCA-Level5, RHCDS, RHCSS, RHCVA, RHCE Expertise in Linux Troubleshooting & Tuning

Sachin Yadav

Sachin Yadav

5+ Years Experience B.Tech, RHCE, Python Certified Expertise in R & Python ,Data science & Artificial Intelligence

Nidhi

Nidhi Sharma

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Mr. Pawan Khatri

Pawan Khatri

4+ Years Experience MCA, Career Counselor Expertise in Soft Skills Training

Nikhil Maheshwari

2.5 + years ExperienceB.Tech, RHCSA ,RHCEExpertise in Redhat Linux &Cloud computing(AWS)

Nidhi Singh Choudhary

4+Years Experience B.Tech , BDM , Softskills TrainerExpertise in Career Counselling and Softskills

Kushal Samota

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Naveen Singh

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Rupesh Saini

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3 years experienceMCA Expertise in RHCSA, RHCE, AWS, Azure, GCP, Docker & container, K8, Openstack

Simran Grover

3 Years ExperienceB.TechExpertise in Python, Web Design(html,css,bs), Web Development, Data Science, Machine Learning, Data Analytics, Big Data

Mayank Sharma

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Get training on Linux, Ansible, Devops ,Python , Networking , AWS and Openstack Cloud by Certified Trainers at GRRAS. You would be able to get the best training along with the interview preparation in this course module .

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