Hadoop Online Training

Hadoop Online Training

What is Hadoop Training?

About the course :

More and more organizations are adopting Hadoop to transform into truly data-driven decision making. This makes Hadoop training courses amongst the most sought-after programs in the industry if you wish to become an expert Hadoop developer and administrator. Students learn to develop applications as well as analyze big data stored in Hadoop. Complete details of the Hadoop Distributed File System (HDFS) architecture and MapReduce framework are discussed. Various tools like C#, Pig, Hive, HCatalog, Sqoop, Oozie and MS Excel are also touched upon. There are variety of classroom and online certification classes available which also offer training & placement services. Hadoop modules cover in-depth knowledge and hands-on training in administering Hadoop cluster, Impala, Zookeeper, JAQL and Flume.

What are the topics covered?

•             Java Essentials for Hadoop

•             Linux Essentials for Hadoop

•             Concept of Big Data & Hadoop Hadoop Architecture

•             HDFS Job Tracker, Task Tracker

•             Name & Data Nodes Map-Reduce

•             HIVE PIG

•             JAQL Sqoop

•             Oozie BigInsights

•             Impala Cluster Administration

•             Master Slave Nodes Managing Nodes

•             Cluster Health Check File System Disk Check

•             Administer the Hadoop Services Configuring Hadoop Settings

•             Managing Storage Block Size Managing Data Nodes Usage

•             Managing the replication Factor Data Movement: Through Terminal & Web Console

•             Cloud Computing Essentials Cloud Players

•             How to work with Hadoop on Cloud

Curriculum Followed in Hadoop Training

•             Big Data

•             What is Big Data?

•             Need for Data Analytics

•             Need for Data Analytics

•             Technology and Science behind Data

•             What is Data Processing?

•             Evolution and Trends of Data Processing Technologies

•             Apache Hadoop

•             Overview of Apache Hadoop

•             Apache Hadoop Architecture

•             Apache Hadoop Ecosystem

•             Relational Database Management System (RDBMS) and Apache Hadoop

•             Subprojects in Apache Hadoop

•             Distributions in Apache Hadoop

•             Apache Hadoop Documentation

•             How to setup Apache Hadoop?

Apache Hadoop installation

•             Apache Hadoop Configuration

•             Starting Apache Hadoop

•             How to run Apache Hadoop Clients?

•             How to browse Apache Hadoop User Interface (UI) Consoles

•             Hadoop Distributed File System (HDFS)

•             Architecture of HDFS 1.0

•             Architectural Features

•             Performance

•             Availability

•             Installation Featuresf

•             Configuration Features

•             Usability

•             Security Features

•             Operability

•             Architecture of HDFS 2.0

•             Advanced Programming in Hadoop Distributed File System (HDFS)

•             Compressing Files

•             Decompressing Files

•             Serialization of Types

•             Deserialization of Types

•             File Sequencing

•             What is MapReduce?

•             Architecture of MapReduce

•             MapReduce Architecture of Hadoop 1.0

•             Features of MapReduce Architecture

•             Performance

•             Availability

•             Installation Features

•             Configuration Features

•             Usability

•             Security Features

•             Operability

•             MapReduce Architecture of Hadoop 2.0

•             Basic-level MapReduce Programming

•             Map Phase in MapReduce Programming Concepts

•             Reduce Phase in MapReduce Programming Concepts

•             MapReduce Application Programming Interface (API) - Important Java Classes

•             Hierarchy of Java Classes - MapReduce Application Programming Interface (API)

•             How to write a MapReduce program?

•             Intermediate level MapReduce Programming

•             Counts Settings

•             Mapper Counts Setting

•             Reducer Counts Settings

•             Configuring MapReduce

•             Partitioners and Combiners in MapReduce Programming

•             Speculative Execution

•             Task Java Virtual Machine (JVM) Reuse

•             Compressing Techniques

•             Advanced MapReduce Programming

•             Formats

•             Input Format

•             Output Format

•             Custom Data Format

•             Built-in Mappers

•             Built-in Reducers

•             Distributed Cache Memory

•             Counters

•             Multithreading

•             Pipes and Streaming in MapReduce

•             Apache Hadoop Pipes in MapReduce

•             Apache Hadoop Streaming in MapReduce

•             Databases and Querying

•             Querying Data using Hive

•             Querying Data using Pig

•             HBase Real-time Database

What are the benefits of Hadoop certification?

•             Companies are looking for candidates with Hadoop training and certification

•             Get an edge over other professionals

•             Career advancement

•             Gives credibility to your knowledge

•             Helpful if you are transitioning into Hadoop from other backgrounds

•             Increase your confidence

Who should attend?

It is well-suited for software engineers with a background in ETL/Programming and managers dealing with latest technologies and data management. Also, .NET Developers and data analysts who have to develop applications and perform big data analysis using the Hortonworks Data Platform for Windows will also find this helpful.

What are the pre-requisites?

Prerequisites for learning Hadoop include hands-on experience in Core Java and good analytical skills to grasp and apply the concepts in Hadoop. Students should have programming experience, preferably with Visual Studio and SQL, as well as familiarity with the Windows Server operating system. No prior Hadoop knowledge is required though.

What are the responsibilities of Hadoop professional?

•             Architect and Build Big Data Platform using Hadoop echo system.

•             Evaluate, recommend and build NoSQL, SQL and InMemory platform understanding the business needs.

•             Architect and build data collectors and data fabric to collect and transport data to the Hadoop echo system based Big Data Platform.

•             Build monitoring solutions for the Big Data infrastructure to understand the health of the infrastructure.

•             Automate deployment and configuration management using open source frameworks.

•             Be the SME (Subject Matter Expert) for Big Data Platform(s).