Big Data and Hadoop Training

4.0 (7891) 8720 Learners

Get a deeper knowledge of various Big Data frameworks

Hands-on learning on Big data Analytics with Hadoop

Projects related to banking, governmental sectors, e-commerce websites, etc

Learn to extract information with Hadoop MapReduce using HDFS, Pig, Hive, etc.

Upgrade your career in the field of Big data

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Overview

Wissenhive's Big Data Hadoop Training Course is curated by Hadoop industry experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem tools such as HDFS, YARN, MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume and Sqoop. Throughout this online instructor-led Hadoop Training, you will be working on real-life industry use cases in Retail, Social Media, Aviation, Tourism and Finance domain using Edureka's Cloud Lab.

What you will learn

  • Learn the fundamentals
  • Efficient data extraction
  • MapReduce
  • Debugging techniques
  • Hadoop frameworks
  • Real-world analytics

Benefits

With most businesses facing a data deluge, the Hadoop platform helps in processing these large volumes of data in a rapid manner, thereby offering numerous benefits at both the organization and individual level.

Individual Benefits:

Undergoing training in Hadoop and big data is quite advantageous to the individual in this data-driven world:

  • Enhance your career opportunities as more organizations work with big data
  • Professionals with good knowledge and skills in Hadoop are in demand across various industries
  • Improve your salary with a new skill-set. According to ZipRecruiter , a Hadoop professional earns an average of $133,296 per annum
  • Secure a position with leading companies like Google, Microsoft, and Cisco with skills in Hadoop and big data

Organizational Benefits:

Training in Big Data and Hadoop has certain organizational benefits as well:

  • Relative to other traditional solutions, Hadoop is quite cost-effective because of its seamless scaling capabilities across large volumes of data
  • Expedited access to new data sources which allows an organization to reach its full potential
  • Boosts the security of your system as Hadoop boasts of a feature called HBase security
  • Hadoop enables organizations to run applications on thousands of nodes

Given the ease with which it allows you to make sense of huge volumes of data and leverage frameworks to transform the same into actionable insights, training and certification courses for Hadoop & Big Data are in great demand in the field of data science.

Course Content

Understanding Big Data 14:51 Play
Types of Big Data 14:51 Play
Difference between Traditional Data and Big Data 14:51 Play
Introduction to Hadoop 14:51 Play
Distributed Data Storage In Hadoop, HDFS and Hbase 14:51 Play
Hadoop Data processing Analyzing Services MapReduce and spark, Hive Pig and Storm 14:51 Play
Data Integration Tools in Hadoop 14:51 Play
Resource Management and cluster management Services 14:51 Play
Need of Hadoop in Big Data 14:52 Play
Understanding Hadoop And Its Architecture 14:52 Play
The MapReduce Framework 14:52 Play
What is YARN? 14:52 Play
Understanding Big Data Components 14:53 Play
Monitoring, Management and Orchestration Components of Hadoop Ecosystem 14:53 Play
Different Distributions of Hadoop 14:53 Play
Installing Hadoop 3 14:53 Play
Hortonworks sandbox installation & configuration 16:49 Play
Hadoop Configuration files 16:49 Play
Working with Hadoop services using Ambari 16:49 Play
Hadoop Daemons 16:49 Play
Browsing Hadoop UI consoles 16:49 Play
Basic Hadoop Shell commands 16:49 Play
Eclipse & winscp installation & configurations on VM 16:49 Play
Running a MapReduce application in MR2 13:24 Play
MapReduce Framework on YARN 13:24 Play
Fault tolerance in YARN 13:24 Play
Map, Reduce & Shuffle phases 13:24 Play
Understanding Mapper, Reducer & Driver classes 13:24 Play
Writing MapReduce WordCount program 13:24 Play
Executing & monitoring a Map Reduce job 13:24 Play
SparkSQL and DataFrames 13:25 Play
DataFrames and the SQL API 13:25 Play
DataFrame schema 13:25 Play
Datasets and encoders 13:25 Play
Loading and saving data 13:25 Play
Aggregations 13:25 Play
Joins 13:25 Play
A short introduction to streaming 13:27 Play
Spark Streaming 13:27 Play
Discretized Streams 13:27 Play
Stateful and stateless transformations 13:27 Play
Checkpointing 13:27 Play
Operating with other streaming platforms (such as Apache Kafka) 13:27 Play
Structured Streaming 13:27 Play
Background of Pig 13:28 Play
Pig architecture 13:28 Play
Pig Latin basics 13:29 Play
Pig execution modes 13:29 Play
Pig processing – loading and transforming data 13:29 Play
Pig built-in functions 13:29 Play
Filtering, grouping, sorting data 13:29 Play
Relational join operators 13:29 Play
Pig Scripting 13:29 Play
Pig UDF's 13:29 Play
Background of Hive 13:31 Play
Hive architecture 13:31 Play
Hive Query Language 13:31 Play
Derby to MySQL database 13:31 Play
Managed & external tables 13:31 Play
Data processing – loading data into tables 13:31 Play
Hive Query Language 13:31 Play
Using Hive built-in functions 13:31 Play
Partitioning data using Hive 13:31 Play
Bucketing data 13:31 Play
Hive Scripting 13:31 Play
Using Hive UDF's 13:31 Play
HBase overview 13:33 Play
Data model 13:33 Play
HBase architecture 13:33 Play
HBase shell 13:33 Play
Zookeeper & its role in HBase environment 13:33 Play
HBase Shell environment 13:33 Play
Creating table 13:33 Play
Creating column families 13:33 Play
CLI commands – get, put, delete & scan 13:33 Play
Scan Filter operations 13:33 Play
Importing data from RDBMS to HDFS 13:34 Play
Exporting data from HDFS to RDBMS 13:34 Play
Importing & exporting data between RDBMS & Hive tables 13:35 Play
Overview of Oozie 13:37 Play
Oozie Workflow Architecture 13:37 Play
Creating workflows with Oozie 13:37 Play
Introduction to Flume 13:37 Play
Flume Architecture 13:37 Play
Flume Demo 13:37 Play
Introduction 13:38 Play
Tableau 13:38 Play
Chart types 13:38 Play
Data visualization tools 13:38 Play
Cloud computing basics 13:39 Play
Concepts and terminology 13:39 Play
Goals and benefits 13:39 Play
Risks and challenges 13:39 Play
Roles and boundaries 13:39 Play
Cloud characteristics 13:39 Play
Cloud delivery models 13:39 Play
Cloud deployment models 13:39 Play

Course Details

Hadoop is an Apache project (i.e. an open source software) to store & process Big Data. Hadoop stores Big Data in a distributed & fault tolerant manner over commodity hardware. Afterwards, Hadoop tools are used to perform parallel data processing over HDFS (Hadoop Distributed File System).

As organisations have realized the benefits of Big Data Analytics, so there is a huge demand for Big Data & Hadoop professionals. Companies are looking for Big data & Hadoop experts with the knowledge of Hadoop Ecosystem and best practices about HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, Sqoop & Flume. 

Wissenhive Hadoop Training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop Ecosystem. This Hadoop developer certification training is stepping stone to your Big Data journey and you will get the opportunity to work on various Big data projects.

Who Should attend?

  • Data Architects
  • Data Scientists
  • Developers
  • BI Developers
  • BI Analysts
  • SAS Developers
  • Data Analysts

Course Info.

Lenght
30+ hours
Effort
2-3 hours/week
Institution
Open Source
Language
English
Video Script
English

Training Options

Selfpaced Training

299
  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 3 simulation test papers for self-assessment
  • Lab access to practice live during sessions
  • 24x7 learner assistance and support

Live Virtual Classes

499
  • Online Classroom Flexi-Pass
  • Lifetime access 
  • Practice lab and projects with integrated Azure labs
  • Access to Microsoft official content aligned to examination

One on One Training

999
  • Customized learning delivery model (self-paced and/or instructor-led)
  • Flexible pricing options
  • Enterprise grade learning management system (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Exam & Certification

No Exam Required.

you will be required to complete a project which will be assesd by our certified instructors. on succesful completion of the project you will be awarded a training certificate.

Big Data and Hadoop Training

FAQs

The Big Data Analytics course sets you on your path to become an expert in Big Data Analytics by understanding its core concepts and learning the involved technologies. Most of the courses will also involve you working on real-time and industry-based projects. Through an intensive training program, you will learn the practical applications of the field.

Today, the job market is saturated and there is immense competition. Without any specialization, chances are that you will not be considered for the job you are aspiring for.
Big Data Hadoop is used across enterprises in various industries and the demand for Hadoop professionals is bound to increase in the future. Certification is a way of letting recruiters know that you have the right Big Data Hadoop skills they are looking for. With top corporations bombarded with tens of thousands of resumes for a handful of job postings, a Hadoop certification helps you stand out from the crowd. A Certified Hadoop Administrator also commands a higher pay in the market with an average annual income of $123,000. Hadoop certifications can thus propel your career to the next level.

Here are the main differences between Hadoop and Big Data:

  • Accessibility - It is difficult to access Big data, whereas you can use the Hadoop framework for processing and accessing data at a faster rate.
  • Storage - Storing Big Data is extremely difficult as it is usually in structured and unstructured form. Apache Hadoop HDFS can be used to store big data.
  • Significance - Big Data doesn’t have any value on its own until it can be used for creating profit after data processing. Hadoop is the framework that can make Big data meaningful.
  • Definition - Big Data is just a large volume of data present in structured and unstructured form. Hadoop is a framework responsible for handling Big Data and processing it.
  • Developers - Big data developers develop applications in Pg, MapReduce, Spark, Hive, etc. Hadoop developers are responsible for coding that will be used for processing the data.
  • Type - Big data is a problem that doesn’t have any value or meaning unless it is processed. Hadoop is a solution that solves Big Data complex processing.