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Hadoop Big Data And Sap Hana Demo On 22Nd Nov At 6.30 Am(Ist)


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Contact No+91 9036 298 699 (or) +91 78 29 29 7899



Hadoop demo on 21st Nov At 8 PM(EST) || 22nd Nov at 6.30 AM(IST) by certified trainer.

 

 

Hadoop Introduction
 
 Introduction to Data and System
 Data Lifecycle Management
 Data Properties
 Types of Data
 Introduction of system
 Problems with traditional large-scale systems
 Types of Systems & scaling
 What is Big Data
 Challenges in Big Data
 Challenges in Traditional Application
 New Requirements
 What is Hadoop
 Brief history of Hadoop
 Features of Hadoop
 Hadoop v/s RDBMS
 Hadoop Ecosystem’s overview
 
Administrate Hadoop Distribute File System(HDFS)
 
 Concepts
 Blocks
 Replication
 Version File
 Safe mode
 Namespace IDs
 Reading and Writing in HDFS
 Understanding Name Node
 Understanding Data Node
 Understanding Secondary Name Node
 Understanding Job Tracker
 Understanding Task Tracker
 HDFS Shell Commands
 Hadoop Admin Commands
 Hands On Exercise
 Accessing HDFS using API
 Understanding HDFS Java classes and methods
 HDFS Nextgeneration Concepts.
 Hands On Exercise
 
Setting up Hp Hp Hadoop Cluster for Apache Hadoop
 
 Installation in detail
 Creating Ubuntu image in VMware
 Downloading Hadoop
 Installing SSH
 Configuring Hadoop
 Download ,Installation & Configuration Hive
 Download ,Installation & Configuration Pig
 Download ,Installation & Configuration Sqoop
 Download ,Installation & Configuration Hive
 Installing MySql in Hadoop cluster.
 Download and work with Cloudera Image.
 
Configuring Hadoop in Different Modesdoop
 Local Mode
 Running without HDFS
 Pseudo-distributed Mode
 Running all daemons in a single node
 Fully distributed mode
 Running daemons on dedicated nodes
 
Cluster Maintenance
 Managing Hadoop Processes
 Starting and Stopping Processes with Init Scripts
 Starting and Stopping Processes Manually
 HDFS Maintenance Tasks
 Adding a Datanode
 Decommissioning a Datanode
 Checking Filesystem Integrity with fsck
 Balancing HDFS Block Data
 Dealing with a Failed Disk
 MapReduce Maintenance Tasks
 Adding a Tasktracker
 Decommissioning a Tasktracker
 Killing a MapReduce Job
 Killing a MapReduce Task
 Dealing with a Blacklisted Tasktracker
 
Map Reduce Programming
 Understanding block and input splits
 Common Input and Output Formats
 MapReduce Data types
 Understanding Writable and WritableComparable (Introduction)
 Data Flow in MapReduce Application
 Understanding MapReduce problem on real datasets(stocks).
 MapReduce Skeleton in Details
 Writing MapReduce Application
o Understanding Mapper function
o Understanding Reducer Function
o Understanding Driver
 Understanding Tool Runner
 Hands on Exercise
 MapReduce Continued
 Using Combiner
 Using Distributed Cache
 Passing the parameters to mapper and reducer
 Hands On Exercise
 Writing Custom key values
 Hands On Exercise
 Designed Use Cases for common problems.
 
Advanced MapReduce Programming 
 MapReduce Chaining.
 Customized Input Formats and Output Formats
 
Monitoring  and debugging on a Production Cluster
 Counters
 Skipping Bad Records
 Running in local mode
 
Tuning for Performance in MapReduce 
 Reducing network traffic with combiner
 Partitioners
 Reducing the amount of input data
 Using Compression
 Reusing the JVM
 Running with speculative execution
 
Hive
 Hive concepts
 Hive architecture
 Install and configure hive on cluster
 Different type of tables in hive
 Hive library functions
 Buckets
 Partitions
 Joins in hive
o Inner joins
o Outer Joins
 Hive UDF
 
PIG
 Pig basics
 Install and configure PIG on a cluster
 PIG Library functions
 Pig Vs Hive
 Write sample Pig Latin scripts
 Modes of running PIG
o Running in Grunt shell
 Running as Java program
 PIG UDFs
 
Sqoop
 Install and configure Sqoop on cluster
 Connecting to RDBMS
 Installing Mysql
 Import data from Oracle/Mysql to hive
 Export data to Oracle/Mysql
 Internal mechanism of import/export
 
HBase
 HBase concepts
 HBase architecture
 Region server architecture
 File storage architecture
 HBase basics
 Column access
 Scans
 HBase use cases
 Install and configure HBase on a multi node cluster
 Create database, Develop and run sample applications
 Access data stored in HBase using clients like Java, Python and Pearl
 Map Reduce client to access the HBase data
 

HIGHLIGHTS

 INTERVIEW QUESTIONS
 SAMPLE RESUMES

 

 

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