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Best Hadoop Online Training in USA | UK| Canada | India by Experts

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From our Hadoop Online Training learner can understand the fundamental concepts of Hadoop Tool. Our training program is packed with tips, exercises, hints and examples. Our training sessions makes you to learn Servicenow quickly and effectively and also helps you to pass Bigdata Certification easily. Contact for more details India: +91-9642373173, USA: : +1-845-915-8712, Mail: info@svsoftsolutions.com – PowerPoint PPT presentation

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Title: Best Hadoop Online Training in USA | UK| Canada | India by Experts


1
ONLINE TRAINING
Contact 91-9642373173 Mail
info_at_svsoftsolutions.com USA 1-845-915-8712
Visit www.svsoftsolutions.com
2
About Hadoop Online Training
  • SV Soft Solutions is best Hadoop Training
    institute in Hyderabad. SV Soft Solutions have
    training for all groups of people from learner to
    advanced level professionals. We have trained
    more than 1000 students around the world and
    placed them in 500 fortune companies. Our Hadoop
    Online Training will be provided worldwide like
    USA, UK, Canada and India. We provide best Hadoop
    Online Certification Training with real time and
    job oriented sessions.

3
Hadoop Training Highlights
  • Detailed course material with real time examples
  • Hadoop classes with expert trainers
  • 24x7 customer support
  • We are giving placement support in companies in
    USA, Canada and India
  • Certification oriented Hadoop training

4
Who Should Take Hapood Training?
  • System Administrators
  • Programing Developers
  • Project Managers
  • IT Professionals
  • Testing Professionals
  • Mainframe Working Professionals
  • Graduates or Undergraduates who are interested to
    learn Hadoop Online Course

5
Hadoop Course Benefits
  • Industry Expert Trainer
  • Real time Scenario
  • Doubt session Support
  • Topic to Topic Assignments
  • Resume Preparation
  • Interview Q/A
  • Record Sessions
  • Detailed Study Material
  • 24/7 Support

6
About Trainer
  • Big Data Hadoop Training will be delivered by
    well qualified, professional who have good
    experience to handle any kind of Hadoop issues.
    Our Trainers are officially working in MNC, so
    they share their experience with learners.

7
Hadoop Online Training Syllabus
  • Understanding BigData
  • What is Big Data?
  • Big-Data characteristics
  • Hadoop Distributions
  • Hortonworks
  • Cloudera
  • Pivotal HD
  • Greenplum

8
  • Introduction to Apache Hadoop
  • Flavors of Hadoop Big-Insights, Google Query
    etc..
  • Hadoop Eco-system components Introduction
  • MapReduce
  • HDFS
  • Apache Pig
  • Apache Hive

9
  • HBASE
  • Apache Oozie
  • FLUME
  • SQOOP
  • Apache Mahout
  • KIJI
  • LUCENE
  • SOLR

10
  • KiteSDK
  • Impala
  • Chukwa
  • Shark
  • Cascading
  • Understanding Hadoop Cluster
  • Hadoop Core-Components
  • NameNode
  • JobTracker
  • TaskTracker
  • DataNode
  • SecondaryNameNode

11
  • HDFS Architecture
  • Why 64MB?
  • Why Block?
  • Why replication factor 3?
  • Discuss NameNode and DataNode
  • Discuss JobTracker and TaskTracker
  • Typical workflow of Hadoop application
  • Rack Awareness
  • Network Topology
  • Assignment of Blocks to Racks and Nodes
  • Block Reports
  • Heart Beat
  • Block Management Service

12
  • Anatomy of File Write
  • Anatomy of File Read
  • Heart Beats and Block Reports
  • Discuss Secondary NameNode
  • Usage of FsImage and Edits log
  • Map Reduce Overview
  • Best Practices to setup Hadoop cluster

13
  • Cluster Configuration
  • Core-default.xml
  • Hdfs-default.xml
  • Mapred-default.xml
  • Hadoop-env.sh
  • Slaves
  • Masters
  • Need of -site.xml
  • Map Reduce Framework
  • Why Map Reduce?
  • Use cases where Map Reduce is used

14
  • Hello world program with Weather Use Case
  • Setup environment for the programs
  • Possible ways of writing Map Reduce program with
    sample codes find the best code and discuss
  • Configured, Tool, GenericOptionParser and queues
    usage
  • Demo for calculating maximum temperature and
    Minimum temperature
  • Limitations of traditional way of solving word
    count with large dataset
  • Map Reduce way of solving the problem
  • Complete overview of MapReduce
  • Split Size
  • Combiners

15
  • Multi Reducers
  • Parts of Map Reduce
  • Algorithms
  • Apache Hadoop Single Node Installation Demo
  • Namenode format
  • Apache Hadoop Multi Node Installation Demo
  • Add nodes dynamically to a cluster with Demo
  • Remove nodes dynamically to a cluster with Demo
  • Safe Mode
  • Hadoop cluster modes

16
  • Standalone Mode
  • Psuedo distributed Mode
  • Fully distributed mode
  • Revision
  • HDFS Practicals(HDFS Commands)
  • Map Reduce Anatomy
  • Job Submission
  • Job Initialization
  • Task Assignments
  • Task Execution

17
  • Schedulers
  • Quiz
  • Map Reduce Failure Scenarios
  • Speculative Execution
  • Sequence File
  • Input File Formats
  • Output File Formats
  • Writable DataTypes
  • Custom Input Formats
  • Custom keys, Values usage of writables
  • Walkthrough the installation process through the
    cloudera manager
  • Example List, show sample example list for the
    installation
  • Demo on teragen, wordcount, inverted index,
    examples

18
  • Debugging Map Reduce Programs
  • Map Reduce Advance Concepts
  • Partitioning and Custom Partitioner
  • Joins
  • Multi outputs
  • Counters
  • MR unit testcases
  • MR Design patterns
  • Distributed Cache
  • Command line implementation
  • MapReduce API implementation

19
  • Map Reduce Advance concepts examples
  • Introduction to course Project
  • Data loading techniques
  • Hadoop Copy commands
  • Put,get,copyFromLocal,copyToLocal,mv,chmod,rmr,rmr
    skipTrash,distcp,ls,lsr,df,du,cp,moveFromLocal,m
    oveToLocal,text,touhz,tail,mkdir,help
  • Flume
  • Sqoop
  • Demo for Hadoop Copy Commands
  • Sqoop Theory
  • Demo for Sqoop
  • Need of Pig?
  • Why Pig Created?

20
  • Introduction to skew Join
  • Why go for Pig when Map Reduce is there?
  • Pig use cases
  • Pig built in operators
  • Pig store schem
  • Operators
  • Load
  • Store
  • Dump
  • Filter
  • Distinct
  • Group
  • CoGroup
  • Join

21
  • Foreach Generate
  • Parallel
  • Distinct
  • Limit
  • ORDER
  • CROSS
  • UNION
  • SPLIT
  • Sampling
  • Dump Vs Store
  • DataTypes
  • Complex
  • Bag
  • Tuple
  • Atom
  • Map

22
  • Primitives
  • Integers
  • Float
  • Chararray
  • byteArray
  • Double
  • Diagnostic Operators
  • Describe
  • Explain
  • Illustrate

23
  • UDFs
  • Filter Function
  • Eval Function
  • Macros
  • Demo
  • Storage Handlers
  • Pig Practicals and Usecases
  • Demo using schema
  • Demo using without schema
  • Hive Background
  • What is Hive?
  • Pig Vs Hive
  • Where to Use Hive?
  • Hive Architecture
  • Metastore
  • Hive execution modes
  • External, Manged, Native and Non-native tables

24
  • Hive Partitions
  • Dynamic Partitions
  • Static Partitions
  • Buckets
  • Hive DataModel
  • Hive DataTypes
  • Primitive
  • Complex
  • Queries
  • Create Managed Table
  • Load Data
  • Insert overwrite table
  • Insert into Local directory
  • CTAS
  • Insert Overwrite table select

25
  • Joins
  • Inner Joins
  • Outer Joins
  • Skew Joins
  • Multi-table Inserts
  • Multiple files, directories, table inserts
  • Serde
  • View
  • Index
  • UDF
  • UDAF
  • Hive Practicals
  • Oozie Architecture
  • Workflow designing in Oozie
  • Oozie practicals
  • YARN Architecture
  • Hadoop Classic vs YARN
  • YARN Demo

26
  • Flume Architecture
  • Flume Practicals
  • Zoo Keeper
  • Introduction to NOSQL Databases
  • NOSql Landscapes
  • Introduction to HBASE
  • HBASE vs RDBMS
  • Create Table on HBASE using HBASE shell
  • Where to use HBASE?
  • Where not to use HBASE?
  • Write Files to HBASE

27
  • Major Components of HBASE
  • HBase Master
  • HRegionServer
  • HBase Client
  • Zookeeper
  • Region
  • HBase Practicals
  • HBASE ROOT- Catalog table
  • CAP Theorm
  • Compaction
  • Sharding
  • Sparse Datastore
  • Cassandra Architecture
  • Big Table and Dynamo
  • Distributed Hash Table, P2P Fault Toleranta

28
  • Data Modelling
  • Column Families
  • Installation Demo on Cassandra
  • Practicals
  • Real time Project Analysis
  • Design
  • Implementation
  • Execution
  • Debugging
  • Optimization Techniques
  • Which one to use where
  • Amazon Web Services(Hadoop on Cloud)
    Installations for MultiNode
  • EMR and S3
  • Storm Architecture
  • Real time use case with Storm

29
  • Spark
  • What is Spark?
  • Understanding Spark
  • Spark Architecture
  • RDD
  • Hadoop RDD
  • RDDs Partitioning
  • Lazy Evaluation
  • Caching
  • Spark Context
  • Map, flatMap, filter
  • Actions
  • Serialization
  • Scala
  • Scala Features
  • Scala Functions
  • Collections and Combiners
  • Spark with Scala
  • Spark with Yarn

30
  • Spark on Cluster mode
  • Spark CLI
  • Spark programming with Java API
  • Spark Streaming
  • Spark SQL
  • Spark SQL Context
  • Spark SQL with Hive
  • Spark MLib Algorithms(K-Means, Clustering,..)
  • Spark GraphX Overview
  • Hands On and Usecases
  • Impala Architecture
  • Impala Practicals
  • Adhoc Querying in Impala

31
  • Compression Techniques
  • Snappy
  • LZO
  • Bgzip
  • Image processing in Hadoop
  • Certification Preparation Guidelines
  • Best Practices to setup Hadoop cluster
  • Commissioning and Decommissioning Nodes
  • Benchmarking the Hadoop cluster
  • Admin monitoring tools
  • Routine Admin tasks
  • Kafka Architecture
  • Kafka Usecase Execution

32
Contact Us
  • For More Information about training
  • Website www.svsoftsolutions.com
  • Email info_at_svsoftsolutions.com
  • Phone USA 1-845-915-8712
  • India 91-9642373173
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