About Hadoop Training:
Hadoop Training will build your skills in administration and development. Hadoop is a free source, java based programming framework that assists the processing and storage of highly large data sets in a distributed computing environment. Hadoop belongs to Apache project invented by the Apache Software Foundation.
Hadoop tutorial is prepared to cover both the Hadoop administration and development skills. Hadoop Big Data Training prepared you to get Hadoop certification.
Why Learn Hadoop?
This course combines Hadoop Admin and Hadoop Development objectives. Hadoop Admin covers Configuring, Deploying and Maintaining a Hadoop Cluster. When you learn about Hadoop Development, MapReduce application in Java programming is dealt in detail. Hadoop Development course also includes debugging, managing jobs, improving performance, working with custom data, managing workflows, and using other programming languages. Through the training, you can get better practical hands-on exercise, project work guidance, and open discussion on Hadoop large data sets in enterprises. You can prepare for Hadoop administrator certification.
By participating in this hadoop admin training course with us you can learn more about administrator and development aspects in Hadoop environment. Kernel Training has designed classroom and online course to upgrade your real time analytics knowledge and skills on Hadoop Admin and Hadoop Development. In Hadoop Admin you can learn about Hadoop Cluster in a development or production environment. And, from the Hadoop Development training you can understand as how to write effective MapReduce applications in Java and scripting languages. Know whether to be Hadoop admin or developer.
Hadoop Admin & Developer Course Objective
- Introduction to Big Data, Hadoop & HDFS & Administration
- Hadoop Map-reduce concepts and features
- Hadoop Administration concepts
- Apache Hive
- Apache Pig
- Apache Flume & Sqoop
- Apache HBASE Concepts, YARN & MR V2
- Apache Oozie workflow concepts
- Introduction to Apache Storm
- Introduction to Apache Spark
- Real World Final Project Banking POC
Course Targeted Audience:
The Hadoop Development and Admin course targets System Administrators, IT managers and Support Engineers who can work on clusters in production or development environments. In addition to, the course also targets developers who want to work on Java MapReduce applications for Hadoop 2.0.
- Prior knowledge about of this field not required.
- Graduates with some prior experience in Core Java and good analytical skills can undergo Hadoop analysis training.
- Basic knowledge of UNIX, sql scripting.
- Well applicable for people proficient with Object Oriented Programming language (OOPS) and JAVA.
Hadoop Training Format:
- Training at Kernel Technology will be instructor-led lecture and discussion.
- Course will end with hands-on labs experience along with project completion.
- Certification of the course after successful completion of quiz.
Companies Hiring Hadoop Admin:
Some of the prominent MNCs are Amazon Web Services, IBM, Hortonworks, Cloudera, Intel, Microsoft, Pivotal, Twitter, Salesforce, AT&T Stumbleupon, Ebay, Yahoo, Facebook, etc.
Hadoop Development and Admin are among the world most in-demand and highly-compensated technical roles. Find details regarding Hadoop administration salary.
Hadoop Training Course Curriculum:
1. Introduction to Big Data and Hadoop
Goal set: In this module, you will get complete information on big data, implementation of Hadoop, and its use cases, Hadoop eco-system, HDFS, MapReduce, Hadoop examples etc.
Topics- Introduction- Big Data, Big Data challenges, Big Data support, History, use cases, Hadoop eco-system, HDFS, MapReduce, Statistics.
2. Understanding the Cluster Administration
Goal set: This module of Hadoop admin training, explains in detail the understanding of cluster administration that includes typical workflow, read/write HDFS files, Anatomy Read/Write, Replication pipeline, and data processing in detail.
Topics- Introduction – Typical workflow, Writing files to HDFS & Reading files from HDFS, Rack Awareness, Overview- A typical learn Hadoop Cluster, Data Loading into HDFS, Introduction – Hadoop Cluster Administrator: Roles and Responsibilities, Introduction – Hadoop server roles and their usage, Rack Awareness, Anatomy of Write and Read, Replication Pipeline, what is hadoop developer, Data Processing
3. Map Reduce
Goal set: In this module of Hadoop developer training, you can understand about mapreduce and map reduce applications, running on clusters, hands on exercises and even more in detail.
Topics- Introduction – Before Map reduce, Overview- Map Reduce, problem, Word Count Find Solution, Map Reduce Flow, Simple problems, Algorithms for complex problems, developing the Map Reduce Application, Data Types, File Formats, Explain – Driver, Mapper and Reducer code, Configuring development environment – Eclipse, Writing Unit Test locally, Running on Cluster, Hands on exercises.
4. How Map-Reduce Works
Goal set: This module deals with the entire working techniques of Map-reduce and you will be provided with hands of exercise in plenty.
Topics- Introduction – Anatomy of Map Reduce job run, Submission, job Initialization, Task Assignment, Completion, Scheduling, Job Failures, Shuffle and sort, • Hands on Exercises.
5. Map Reduce Types and Formats
Goal set: This module describes in detail the various types of Map Reduce, input formats, output formats, and provides you with hands on experience.
Topics – Introduction – Map Reduce Types, Fundamentals – Input Formats – Input splits & records, text input, binary input, multiple inputs & database input Output Formats – text Output, binary output, multiple outputs, lazy output and database output, Labs – Hands on Exercises.
6. Map Reduce Features
Goal set: In this module of Hadoop developer online training you will find about map reduce features in detail and gain hands on experience.
Topics- Introduction – Map Reduce Features, Counters, Combiner, Sorting, Practitioner, Explain – Joins – Map Side and Reduce Side, Hadoop administration commands. Map Reduce Distributed Cache, Side Data Distribution, Labs – Hands Exercises
Hadoop Administration basics:
1. Installation [SQOOP, Pig and pig Latin, HBASE, Hadoop 2.0, MRv2 and YARN Apache Flume]
Goal set: In this module of Hadoop big data, you can learn about Hadoop installation, configuration of SQOOP, Pig and pig Latin, HBASE, Hadoop 2.0, MRv2 and YARN Apache Flume, and also understand on Hadoop ecosystem components.
Topics- Introduction – Hadoop Installation and Initial Configuration, Hadoop Deployment- pseudo-distributed mode, multi-node Hadoop cluster, Installing Hadoop Clients, Configuring Secondary NameNode, Hadoop 2.0, YARN framework, MRv2, Hadoop 2.0 Cluster setup, Planning the Hadoop Cluster: Cluster Size, Hardware and Software considerations, Managing and Scheduling Jobs, Explain – Types of schedulers in Hadoop, Configuring the schedulers, Cluster Monitoring and Troubleshooting, Configure Rack awareness, Setting up Hadoop Backup & Recovery, Whitelist and blacklist data nodes in a cluster, Upgrade Hadoop cluster, Copy data across clusters using distcp, Hadoop administration documentation, Diagnostics and Recovery, Understand- Problem, Plan, Design, and Create a Hadoop Cluster for a Real World Use Case Setup and Configure commonly used Hadoop ecosystem components such as Pig and Hive and Daemons.
Goal set: In this module you can understand the typical case clusters plan Hadoop cluster, schedulers, etc in detail.
Topics: Introduction- Cluster: A Typical Case, Plan Your Hadoop Cluster, Schedulers, (FIFO SCHEDULER, FAIR SCHEDULER, CAPACITY SCHEDULER), Routine Admin Procedures, • Backup and Recovery, Check pointing, Tools, Upgrading, User Accounts & Quotes, Commissioning & Decommissioning nodes, Recover from Application level Problems, Trash Server, Safe mode, Log Files, Admin use case, Benchmarking the Cluster.
Goal set: In this module you can learn about Hive fundamentals, to integration, data manipulation with Hive, and static and dynamic partitioning in detail.
Topics: Introduction – Hive Fundamentals & Architecture, Apache Hive Installation, Loading and Querying Data in Hive, Hive Architecture and Installation, Comparison with Traditional Database, HiveQL: Data Types, Operators and Functions, Hive Tables ,Managed Tables and External Tables, Partitions and Buckets, Storage Formats, Importing Data, Altering Tables, Dropping Tables, Querying Data, Sorting and Aggregating, Map Reduce Scripts, Joins & Sub queries, Views, When to Use HIVE, Impala and Pig, Hands on Exercises, Integration, Data manipulation with Hive, User Defined Functions,Appending Data into existing Hive Table, Static partitioning vs dynamic partitioning
Goal set: In this module you can understand the basics of Sqoop and also usage of queries and Sqoop in detail.
Topics: Introduction – Sqoop, MySQL Client & server, Connecting to relational data base using Sqoop, Importing data using Sqoop from Mysql, Exporting data using Sqoop to MySql, Incremental append, Importing data using Sqoop from Mysql to hive, Exporting data using Sqoop to MySql from hive, Importing data using Sqoop from Mysql to hbase, Using queries and sqoop.
5. Pig and Pig Latin
Goal set: After completion of this module you can understand about pig and pig latin, viewing the schema, and most commonly used functions and practical experience on pig for ETC processing and even more.
Topics: Introduction – Pig, Features & Pig Use Cases, Interacting with Pig, Basic Data Analysis, Pig Latin Syntax, Loading Data, Simple Data Types, Field Definitions, Data Output, Viewing the Schema, Filtering and Sorting Data, Commonly-Used Functions, Hands-On Exercise: Pig for ETL Processing, Storage Formats, Complex/Nested Data Types, Grouping, Built-in Functions for Complex Data, Iterating Grouped Data, Hands-On Exercises, Multi-Dataset Operations with Pig, Techniques for Combining Data Sets, Joining Data Sets in Pig, Splitting Data Sets, Advance commands using Pig for DWH, Hands-On Exercise, Processing Complex Data with Pig
Goal set : In this module you can learn on HBase architecture, concepts, and introduction to Hadoop, and several programming and hands on exercise.
Topics: Introduction – CAP Theorem, HBase Architecture and concepts, Introduction to HBase, Client API’s and their features, HBase tables The ZooKeeper Service, Data Model, Operations, Programming and Hands on Exercises.
7. Hadoop 2.0, MRv2 and YARN
Goal set: After completion of the module, you can understand newly added features in Hadoop 2.0 and HDFS Federation also.
Topics: Introduction- Newly added features in Hadoop 2.0, YARN, MRv2, NameNode High Availability, HDFS Federation.
8. Apache Flume
Goal set: After completion of the module you can gain better understanding of Flume, and twitter Data Analysis project.
Topics: Introduction- Flume, uses, Architecture, configurations, Lighting on Master, collector, Agent, Twitter Data Analysis.
Goal set: In this module you will be able to grasp oozie, architecture, configuration, job submissions, properties, and hands on experience.
Topics: Introduction – Oozie, Architecture, configurations, Oozie Job Submission, properties
10. Introduction to Apache Storm
Goal set: In this module you will understand big data analytics, Hadoop for big data analytics, Storm, comparison between Storm and Hadoop.
Topics: Introduction – Big Data Analytics, Batch Vs Real Time, Hadoop for Batch Analytics, Shortcomings of Hadoop, Storm for Real Time Analytics, Introduction – Storm, Use Cases of Storm, Components, Properties, Storm Vs Hadoop.
11. Introduction to Mahout
Goal set: You can learn in detail about Mahout, and creation of Apache Mahout Algorithms.
Topics: Introduction – Mahout, Lighting on Machine Learning (ML), types, Introduction – Categories of Apache Mahout Algorithms
12. Introduction to Apache Spark
Goal set: In this module, you can learn about Spark, in memory data, and real time analytics options, etc. in detail.
Topics: Introduction – Spark, Batch Vs. Real Time Big Data Analytics, Batch Analytics – Overview – Hadoop Ecosystem, Real Time Analytics Options, Streaming Data – Storm, In Memory Data – Spark, Explain – Spark.
- Final Project
- Final Hadoop Banking Project Environment
- Understand how multiple Hadoop ecosystem components work together in a Hadoop implementation to solve Big Data problems.
- Discuss data sets and specifications of the project.
- Case Studies Discussions.
- Hadoop administration resumes.
- hadoop administration guide.
- Implementation of 2 mini projects in Hive & Pig
- Implementation of a health care use case Hive & Pig
- Final real-time project Banking POC
Our Lab is fully equipped with latest infrastructure with Power 6 and IBM X-series dual Xeon Based servers, EMC clarion, Netapp Unified Storage, Brocade FC switches, Cisco FC switches, Cisco L4 switch, and all the servers are integrated with SAN. All the servers are monitored by Nagios monitoring agent with OTRS ticketing system. You can get prepare for Hadoop administration certification. All the students will have an individual access to the servers through thin clients remotely. Students will be exposed to the real time scenarios through which they can get good exposure on the subject. To help the student in their practical sessions, there will be lab faculties available all the time.
- Resume Preparation
- Placement Assistance
Demo Class Recording
Welcome, To Hadoop Training
How it Works?
- This is an Online Course with Instructor led LIVE and Interactive Sessions, you can access hadoop administration training videos.
- This course contains Practical Work involving Practical Hands-on, Lab Assignments, Hadoop tutorial and Real World Case Studies. This practical work can be done at your own pace.
- You will have access to 24×7 Technical Support. You can request for assistance for any problem you might face or for any clarifications you may require during the course.We may also provide Hadoop administration training PDF.
- At the end of the Hadoop training, you will have to work on a Project to solve a Real-World Python for Big Data Analytics problem. You will receive a Grade and a Verifiable Certificate on the successful completion of this project. Learn learn hadoop administration by real time expert.
Demo class video and PPT Presentation
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