HADOOP ONLINE TRAINING COURSE

KITS Online Training Institute provides best Hadoop Online training by our highly professional trainers. Hadoop is an open course software framework written in Java for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. All modules of Hadoop Online Training are designed with a assumption that hardware failures are common and should be automatically handled by framework. KITS also do corporate trainings and help them to train their employees. We have been offering courses to consultants, companies so that they can meet all the challenges in their respective technologies. Therefore, we also provide similar courses like Hyperion Online Training.

Hadoop Online Training Course Content

Introduction to Hadoop

  • High Availability
  • Scaling
  • Advantages and Challenges 

Introduction to Big Data

  • What is Big data
  • Big Data opportunities
  • Big Data Challenges
  • Characteristics of Big data 

Introduction to Hadoop

  • Hadoop Distributed File System
  • Comparing Hadoop & SQL.
  • Industries using Hadoop.
  • Data Locality.
  • Hadoop Architecture.
  • Map Reduce & HDFS.
  • Using the Hadoop single node image (Clone). 

The Hadoop Distributed File System (HDFS)

  • HDFS Design & Concepts
  • Blocks, Name nodes and Data nodes
  • HDFS High-Availability and HDFS Federation.
  • Hadoop DFS The Command-Line Interface
  • Basic File System Operations
  • Anatomy of File Read
  • Anatomy of File Write
  • Block Placement Policy and Modes
  • More detailed explanation about Configuration files.
  • Metadata, FS image, Edit log, Secondary Name Node and Safe Mode.
  • How to add New Data Node dynamically.
  • How to decommission a Data Node dynamically (Without stopping cluster).
  • FSCK Utility. (Block report).
  • How to override default configuration at system level and Programming level.
  • HDFS Federation.
  • ZOOKEEPER Leader Election Algorithm.
  • Exercise and small use case on HDFS. 

Map Reduce

  • Functional Programming Basics.
  • Map and Reduce Basics
  • How Map Reduce Works
  • Anatomy of a Map Reduce Job Run
  • Legacy Architecture ->Job Submission, Job Initialization, Task Assignment, Task Execution, Progress and Status Updates
  • Job Completion, Failures
  • Shuffling and Sorting
  • Splits, Record reader, Partition, Types of partitions & Combiner
  • Optimization Techniques -> Speculative Execution, JVM Reuse and No. Slots.
  • Types of Schedulers and Counters.
  • Comparisons between Old and New API at code and Architecture Level.
  • Getting the data from RDBMS into HDFS using Custom data types.
  • Distributed Cache and Hadoop Streaming (Python, Ruby and R).
  • YARN.
  • Sequential Files and Map Files.
  • Enabling Compression Codec’s.
  • Map side Join with distributed Cache.
  • Types of I/O Formats: Multiple outputs, NLINEinputformat.
  • Handling small files using CombineFileInputFormat.

Map/Reduce Programming – Java Programming

  • Hands on “Word Count” in Map/Reduce in standalone and Pseudo distribution Mode.
  • Sorting files using Hadoop Configuration API discussion
  • Emulating “grep” for searching inside a file in Hadoop
  • DBInput Format
  • Job Dependency API discussion
  • Input Format API discussion
  • Input Split API discussion
  • Custom Data type creation in Hadoop.

NOSQL

  • ACID in RDBMS and BASE in NoSQL.
  • CAP Theorem and Types of Consistency.
  • Types of NoSQL Databases in detail.
  • Columnar Databases in Detail (HBASE and CASSANDRA).
  • TTL, Bloom Filters and Compensation.

HBase

  • HBase Installation
  • HBase concepts
  • HBase Data Model and Comparison between RDBMS and NOSQL.
  • Master  & Region Servers.
  • HBase Operations (DDL and DML) through Shell and Programming and HBase Architecture.
  • Catalog Tables.
  • Block Cache and sharding.
  • SPLITS.
  • DATA Modeling (Sequential, Salted, Promoted and Random Keys).
  • JAVA API’s and Rest Interface.
  • Client Side Buffering and Process 1 million records using Client side Buffering.
  • HBASE Counters.
  • Enabling Replication and HBASE RAW Scans.
  • HBASE Filters.
  • Bulk Loading and Coprocessors (Endpoints and Observers with programs).
  • Real world use case consisting of HDFS,MR and HBASE.

Hive

  • Installation
  • Introduction and Architecture.
  • Hive Services, Hive Shell, Hive Server and Hive Web Interface (HWI)
  • Meta store
  • Hive QL
  • OLTP vs. OLAP
  • Working with Tables.
  • Primitive data types and complex data types.
  • Working with Partitions.
  • User Defined Functions
  • Hive Bucketed Tables and Sampling.
  • External partitioned tables, Map the data to the partition in the table, Writing the output of one query to another table, Multiple inserts
  • Dynamic Partition
  • Differences between ORDER BY, DISTRIBUTE BY and SORT BY.
  • Bucketing and Sorted Bucketing with Dynamic partition.
  • RC File.
  • INDEXES and VIEWS.
  • MAPSIDE JOINS.
  • Compression on hive tables and Migrating Hive tables.
  • Dynamic substation of Hive and Different ways of running Hive
  • How to enable Update in HIVE.
  • Log Analysis on Hive.
  • Access HBASE tables using Hive.
  • Hands on Exercises

Pig

  • Installation
  • Execution Types
  • Grunt Shell
  • Pig Latin
  • Data Processing
  • Schema on read
  • Primitive data types and complex data types.
  • Tuple schema, BAG Schema and MAP Schema.
  • Loading and Storing
  • Filtering
  • Grouping & Joining
  • Debugging commands (Illustrate and Explain).
  • Validations in PIG.
  • Type casting in PIG.
  • Working with Functions
  • User Defined Functions
  • Types of JOINS in pig and Replicated Join in detail.
  • SPLITS and Multiquery execution.
  • Error Handling, FLATTEN and ORDER BY.
  • Parameter Substitution.
  • Nested For Each.
  • User Defined Functions, Dynamic Invokers and Macros.
  • How to access HBASE using PIG.
  • How to Load and Write JSON DATA using PIG.
  • Piggy Bank.
  • Hands on Exercises

SQOOP

  • Installation
  • Import Data.(Full table, Only Subset, Target Directory, protecting Password, file format other than CSV,Compressing,Control Parallelism, All tables Import)
  • Incremental  Import(Import only New data, Last Imported data, storing Password in Metastore, Sharing Metastore between Sqoop Clients)
  • Free Form Query Import
  • Export data to RDBMS,HIVE and HBASE
  • Hands on Exercises.

HCATALOG

  • Installation.
  • Introduction to HCATALOG.
  • About Hcatalog with PIG,HIVE and MR.
  • Hands on Exercises.

FLUME

  • Installation
  • Introduction to Flume
  • Flume Agents: Sources, Channels and Sinks
  • Log User information using Java program in to HDFS using LOG4J and Avro Source
  • Log User information using Java program in to HDFS using Tail Source
  • Log User information using Java program in to HBASE using LOG4J and Avro Source
  • Log User information using Java program in to HBASE using Tail Source
  • Flume Commands
  • Use case of Flume: Flume the data from twitter in to HDFS and HBASE. Do some analysis using HIVE and PIG

More Ecosystems

  • HUE.(Hortonworks and Cloudera)

Oozie

  • Workflow (Action, Start, Action, End, Kill, Join and Fork), Schedulers, Coordinators and Bundles.
  • Workflow to show how to schedule Sqoop Job, Hive, MR and PIG.
  • Real world Use case which will find the top websites used by users of certain ages and will be scheduled to run for every one hour.
  • Zoo Keeper
  • HBASE Integration with HIVE and PIG.
  • Phoenix
  • Proof of concept (POC).

SPARK

  • Overview
  • Linking with Spark
  • Initializing Spark
  • Using the Shell
  • Resilient Distributed Datasets (RDDs)
  • Parallelized Collections
  • External Datasets
  • RDD Operations
  • Basics, Passing Functions to Spark
  • Working with Key-Value Pairs
  • Transformations
  • Actions
  • RDD Persistence
  • Which Storage Level to Choose?
  • Removing Data
  • Shared Variables
  • Broadcast Variables
  • Accumulators
  • Deploying to a Cluster
  • Unit Testing
  • Migrating from pre-1.0 Versions of Spark
  • Where to Go from Here

Highlights of Hadoop Online training:-

*  Very in depth course material with Real Time Scenarios for each topic with its Solutions for Hadoop Online Trainings.

*  We Also provide Case studies  for Hadoop Online Training.

*  We do Schedule the sessions based upon your comfort by our Highly Qualified Trainers and Real time Experts.

*  We provide you with your recorded session for further Reference.

* We also provide Normal Track, Fast Track and Weekend Batches also for Hadoop Online Training.

* We also provide Cost Effective and Flexible Payment Schemes.

What is Hadoop?

Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. The Hadoop framework application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

Hadoop Architecture

At its core, Hadoop has two major layers namely:

  • Processing/Computation layer (MapReduce), and
  • Storage layer (Hadoop Distributed File System).

MapReduce

MapReduce is a parallel programming model for writing distributed applications devised at Google for efficient processing of large amounts of data (multi-terabyte data-sets), on large clusters (thousands of nodes) of commodity

 hardware in a reliable, fault-tolerant manner. The MapReduce program runs on Hadoop which is an Apache open-source framework.

Hadoop Distributed File System

The Hadoop Distributed File System (HDFS) is based on the Google File System (GFS) and provides a distributed file system that is designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. It is highly fault-tolerant and is designed to be deployed on low-cost hardware. It provides high throughput access to application data and is suitable for applications having large datasets.

Apart from the above-mentioned two core components, Hadoop framework also includes the following two modules:

  • Hadoop Common: These are Java libraries and utilities required by other Hadoop modules.
  • Hadoop YARN: This is a framework for job scheduling and cluster resource.

Advantages of Hadoop

  • Hadoop framework allows the user to quickly write and test distributed systems. It is efficient, and it automatic distributes the data and work across the machines and in turn, utilizes the underlying parallelism of the CPU cores.
  • Hadoop does not rely on hardware to provide fault-tolerance and high availability (FTHA), rather Hadoop library itself has been designed to detect and handle failures at the application layer.
  • Servers can be added or removed from the cluster dynamically and Hadoop continues to operate without interruption.
  • Another big advantage of Hadoop is that apart from being open source, it is compatible on all the platforms since it is Java based.

What you will learn in this Big Data Hadoop training Course?

  • Master fundamentals of Hadoop and YARN and write applications using them
  • Setting up Pseudo node and Multi node cluster on Amazon EC2
  • Master HDFS, MapReduce, Hive, Pig, Oozie, Sqoop, Flume, Zookeeper, HBase
  • Learn Spark, Spark SQL, Streaming, DataFrame, RDD, Graphx, MLlib writing Spark applications
  • Master Hadoop administration activities like cluster managing,monitoring,administration and troubleshooting
  • Practice real-life projects using Hadoop and Apache Spark
  • Be equipped to clear Big Data Hadoop Certification.

Who should take this Big Data Hadoop Online Training Course?

  • Programming Developers and System Administrators
  • Experienced working professionals , Project managers
  • Big DataHadoop Developers eager to learn other verticals like Testing, Analytics, Administration
  • Graduates, undergraduates eager to learn Big Data can take this Big Data Hadoop Certification online training

What are the prerequisites for learning Hadoop?

There is no pre-requisite to take this Big data training and to master Hadoop. But basics of UNIX, SQL and java would be good to learn big data hadoop.

Scheduling Demo With Trainer:

If you would like to take the online demo for Hadoop trainer can you please make an inquiry or fill the form for demo registration, one of our executives will arrange a meeting with the expert trainer.

Course Finished Certificate :

After finish, the course we provide Hadoop course finished certificate of kits technologies looks like

datastage online training

 

 

 

Contact For More Information On Hadoop Online Training                   Hadoop enquiry

Hadoop Online Training Overall rating: ★★★★☆ 4.4 based on 427 reviews
5 1

hadoop enquiry

Request for demo

hadoop enquiry

Abinitio Training

★★★★★
5 5 1
I Have joined for Course with Kits Technologies course.I am completely confident enough in my subject. I never thought Abinitio is SO BIG and huge!!!Loved learning it for about few week and quite happy now. Thanks Kits Team

Sccm

★★★★★
5 5 1
Sccm training done in a good way

Oracle DBA Online Training

★★★★☆
4 5 1
I have completed AWS Online Training From KITS Online Trainings. my trainer such a knowledgeable person. He taught a concepts easy and understandable manner. I am getting day by day update and easy understanding. Thank for the wonderful opportunity

Best Training Instittue

★★★★★
5 5 1
Very nice material and course video available for your reference anytime. I repeatedly watched videos to learn required knowledge. Anyone would definitely miss out the physical presence inside the class while attending the course . I had lot of disturbance at home while class was going on and it didn't give me any seriousness about subject as I didn't see anyone looking at me if I am listening! This factor, someone is looking at me would definitely make me concentrate subject. Also 4 (four) hours of coaching online and very less interaction with people around makes very less impressive learning

My Favourite Training Instittue

★★★★★
5 5 1
My learning with KITS Online Trainings was Golden Gate and SAP Basis training and exam preparation It’s been a worthwhile learning experience - the course was easier to understand than and helped memory retention. Also the online support team were so kind and helpful-when i ran out of time and my exam was a month away they extended my access for extra 30 days. Success all the way

sql server dba

★★★★★
5 5 1
I am Srinivas bolla. I took SQL Database Administrator Online training in Kits online trainings. actually I don’t have knowledge about IT sector, When I was approach Kits online trainings. They gave me well support and training. I learnt a lot from them very well experienced faculty.. very much satisfied for the training. I am happy to say my topics are very clear now. I have no doubt about SQL DBA am very thankful to the whole team of Kits online trainings for providing a Excellent training..

Write A Review 

Name
Email
Review Title
Rating
Review Content

Related Data WareHouse Courses