Hadoop(Development + Admin+ Analytics) Demo On 9Th Oct At 7 Am(Ist)

September 30, 2013

[size=6]Email: [b][color=#ff0000][email protected][/color][/b]

Contact No: [b][color=#ff0000]+91 9036 298 699 (or) +91 78 29 29 7899[/color][/b][/size]

[b][color=#800080][size=6]Hadoop(Development + Admin+ Analytics) demo on 9th Oct At 7 AM(IST) || 10th Oct at 9.30 PM(EST) [/size][/color][color=#800080][size=6]by certified trainer.[/size][/color][/b]

[b]HADOOP BASICS[/b]

 The Motivation for Hadoop
 Problems with traditional large-scale systems
 Data Storage literature survey  Data Processing literature Survey
 Network Constraints  Requirements for a new approach
 Hadoop: Basic Concepts
 What is Hadoop?
 The Hadoop Distributed File System
 Hadoop Map Reduce Works
 Anatomy of a Hadoop Cluster
 Hadoop demons
 Master Daemons
 Name node
 Job Tracker
 Secondary name node
 Slave Daemons
 Job tracker
 Task tracker
 HDFS(Hadoop Distributed File System)
 Blocks and Splits
 Input Splits
 HDFS Splits
 Data Replication
 Hadoop Rack Aware
 Data high availability
 Cluster architecture and block placement

 CASE STUDIES Programming Practices & Performance Tuning
 Developing MapReduce Programs in
 Local Mode Running without HDFS
 Pseudo-distributed Mode Running all daemons in a single node
 Fully distributed mode Running daemons on dedicated nodes
Hadoop Administration
 Setup Hadoop cluster of Apache, Cloudera, Hortonworks, Greenplum
 Make a fully distributed Hadoop cluster on a single laptop/desktop
 Install and configure Apache Hadoop on a multi node cluster in lab.
 Install and configure Cloudera Hadoop distribution in fully distributed mode
 Install and configure Horton Works Hadoop distribution in fully distributed mode
 Install and configure Green Plum distribution in fully distributed mode
 Monitoring the cluster
 Getting used to management console of Cloudera and Horton Works
 Name Node in Safe mode
 Meta Data Backup
 Ganglia and Nagios – Cluster monitoring
 CASE STUDIES

Hadoop Development
 Writing a MapReduce Program
 Examining a Sample MapReduce Program
 With several examples
 Basic API Concepts
 The Driver Code
 The Mapper
 The Reducer
 Hadoop's Streaming API
 Performing several Hadoop jobs

 The configure and close Methods
 Sequence Files
 Record Reader
 Record Writer
 Role of Reporter
 Output Collector  Counters
 Directly Accessing HDFS
 ToolRunner
 Using The Distributed Cache
 Several MapReduce jobs (In Detailed)
 MOST EFFECTIVE SEARCH USING MAPREDUCE
 GENERATING THE RECOMMENDATIONS USING MAPREDUCE
 PROCESSING THE LOG FILES USING MAPREDUCE
 Identity Mapper
 Identity Reducer
 Exploring well known problems using MapReduce applications
 Debugging MapReduce Programs
 Testing with MRUnit
 Logging
 Other Debugging Strategies
.  Advanced MapReduce Programming
 The Secondary Sort  Customized Input Formats and Output Formats
 Joins in MapReduce  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

 Other Performance Aspects  CASE STUDIES  CDH4 Enhancements
 Name Node High – Availability
 Name Node federation
 Fencing  MapReduce Version - 2
HADOOP ANALYST
 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
 Inner joins  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
 Running in Grunt shell  Running as Java program  PIG UDFs  Pig Macros
 Debugging PIG  IMPALA
 Difference between Impala Hive and Pig
 How Impala gives good performance
 Exclusive features of Impala

 Impala Challenges  Use cases of Impala
NOSQL
 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
 HBase and Hive Integration
 HBase admin tasks  Defining Schema and basic operation.
 Cassandra Basics
 MongoDB Basics
Other EcoSystem Components
 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

 Oozie
 Oozie architecture  XML file specifications
 Install and configuring Oozie and Apache
 Specifying Work flow  Action nodes  Control nodes
 Oozie job coordinator
 Flume, Chukwa, Avro, Scribe, Thrift
 Flume and Chukwa concepts
 Use cases of Thrift, Avro and scribe
 Install and configure flume on cluster
 Create a sample application to capture logs from Apache using flume  Hadoop Challenges
 Hadoop disaster recovery
 Hadoop suitable cases

[b]HIGHLIGHTS
 100% CERTIFICATION ASSURANCE
 BIG DATA UNIVERSITY(IBM) CERTIFICATION FREE
 TECHNICAL SUPPORT
 INTERVIEW QUESTIONS
 SAMPLE RESUMES[/b]

October 1, 2013

[color=#282828][font=helvetica, arial, sans-serif][size=4]Email: [email protected][/size][/font][/color]

[color=#282828][font=helvetica, arial, sans-serif][size=4]Contact No: +91 9036 298 699 (or) +91 78 29 29 7899[/size][/font][/color]

October 1, 2013