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Ended last Jun 07, 2017

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SV Soft Solutions is best Hadoop Online 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 online services are provided worldwide like USA, Canada and India. We provide best Hadoop Admin Online Training with real time and job oriented sessions.

Hadoop Online 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

Understanding BigData
- What is Big Data?
- Big-Data characteristics
Hadoop Distributions
- Hortonworks
- Cloudera
- Pivotal HD
- Greenplum
Introduction to Apache Hadoop
- Flavors of Hadoop: Big-Insights, Google Query etc..
Hadoop Eco-system components: Introduction
- MapReduce
- HDFS
- Apache Pig
- Apache Hive
- HBASE
- Apache Oozie
- FLUME
- SQOOP
- Apache Mahout
- KIJI
- LUCENE
- SOLR
- KiteSDK
- Impala
- Chukwa
- Shark
- Cascading
Understanding Hadoop Cluster
Hadoop Core-Components
- NameNode
- JobTracker
- TaskTracker
- DataNode
- SecondaryNameNode
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
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
  - 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
  - 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
  - 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
    - Standalone Mode
    - Psuedo distributed Mode
  - Fully distributed mode
  - Revision
  - HDFS Practicals(HDFS Commands)
  - Map Reduce Anatomy
    - Job Submission
    - Job Initialization
    - Task Assignments
    - Task Execution
  - 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
  - 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
  - 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,moveToLocal,text,touhz,tail,mkdir,help
    - Flume
    - Sqoop
  - Demo for Hadoop Copy Commands
  - Sqoop Theory
  - Demo for Sqoop
  - Need of Pig?
  - Why Pig Created?
  - 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
    - Stream
    - Foreach Generate
    - Parallel
    - Distinct
    - Limit
    - ORDER
    - CROSS
    - UNION
    - SPLIT
    - Sampling
  - Dump Vs Store
  - DataTypes
    - Complex
      - Bag
      - Tuple
      - Atom
      - Map
    - Primitives
      - Integers
      - Float
      - Chararray
      - byteArray
      - Double
  - Diagnostic Operators
    - Describe
    - Explain
    - Illustrate
  - 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
  - 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
  - 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
  - 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
  - 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 Tolerant
  - 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
  - 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
    - 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
  - 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

Schedules

Apr 16, 2017 - Jun 07, 2017

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SV Soft Solutions is a premier IT Training and Placement Institute. We attempt best quality courses in areas of PeopleSoft, SAP, Oracle, Android, Microsoft, Servicenow, Datawarehousing, Hadoop, Amazon Web services and Tibco. Our online training institute is the best place for you to brush up your technical leading and build a good career ahead in IT related technologies. Our main assets are our Trainers who have minimum 8 years of experience in IT. Our training takes place with well prepared training materials. We provide excellent documentation and preparation materials for learners.

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