Hadoop Demo On 21St Oct At 9.30 Pm(Est) By Certified Trainer

October 8, 2013

[color=#282828][font=helvetica, arial, sans-serif][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][/font][/color]

[b][color=#800080][size=6]Hadoop new batch starts on 21st Oct At 9.30 PM(EST) || 22nd Oct at 7 AM(IST) [/size][/color][color=#800080][size=6]by certified trainer.[/size][/color][/b]
[b]HADOOP BASICS[/b]

[color=#282828][font=helvetica, arial, sans-serif] The Motivation for Hadoop [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Problems with traditional large-scale systems[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Data Storage literature survey  Data Processing literature Survey[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Network Constraints  Requirements for a new approach [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Hadoop: Basic Concepts [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] What is Hadoop? [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] The Hadoop Distributed File System[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Hadoop Map Reduce Works [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Anatomy of a Hadoop Cluster[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Hadoop demons [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif]  Master Daemons[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Name node[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Job Tracker[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Secondary name node[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Slave Daemons[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Job tracker [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Task tracker[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] HDFS(Hadoop Distributed File System) [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif]  Blocks and Splits[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Input Splits[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] HDFS Splits[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Data Replication[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Hadoop Rack Aware[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Data high availability [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Cluster architecture and block placement [/font][/color]

[color=#282828][font=helvetica, arial, sans-serif] CASE STUDIES Programming Practices & Performance Tuning [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Developing MapReduce Programs in[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Local Mode Running without HDFS[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Pseudo-distributed Mode Running all daemons in a single node[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Fully distributed mode Running daemons on dedicated nodes [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif]Hadoop Administration [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Setup Hadoop cluster of Apache, Cloudera, Hortonworks, Greenplum [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Make a fully distributed Hadoop cluster on a single laptop/desktop[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Install and configure Apache Hadoop on a multi node cluster in lab.[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Install and configure Cloudera Hadoop distribution in fully distributed mode[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Install and configure Horton Works Hadoop distribution in fully distributed mode[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Install and configure Green Plum distribution in fully distributed mode[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Monitoring the cluster [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Getting used to management console of Cloudera and Horton Works[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Name Node in Safe mode[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Meta Data Backup[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Ganglia and Nagios – Cluster monitoring[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] CASE STUDIES [/font][/color]

[color=#282828][font=helvetica, arial, sans-serif]Hadoop Development [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Writing a MapReduce Program[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Examining a Sample MapReduce Program[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] With several examples[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Basic API Concepts [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] The Driver Code [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] The Mapper [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] The Reducer [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Hadoop's Streaming API[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Performing several Hadoop jobs [/font][/color]

[color=#282828][font=helvetica, arial, sans-serif] The configure and close Methods [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Sequence Files[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Record Reader[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Record Writer[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Role of Reporter[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Output Collector  Counters[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Directly Accessing HDFS[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] ToolRunner[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Using The Distributed Cache[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Several MapReduce jobs (In Detailed) [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] MOST EFFECTIVE SEARCH USING MAPREDUCE[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] GENERATING THE RECOMMENDATIONS USING MAPREDUCE[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] PROCESSING THE LOG FILES USING MAPREDUCE[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Identity Mapper [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Identity Reducer[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Exploring well known problems using MapReduce applications[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Debugging MapReduce Programs [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Testing with MRUnit[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Logging [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Other Debugging Strategies[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif].  Advanced MapReduce Programming [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] The Secondary Sort  Customized Input Formats and Output Formats[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Joins in MapReduce  Monitoring and debugging on a Production Cluster [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Counters  Skipping Bad Records  Running in local mode[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Tuning for Performance in MapReduce [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Reducing network traffic with combiner  Partitioners  Reducing the amount of input data [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Using Compression  Reusing the JVM  Running with speculative execution [/font][/color]

[color=#282828][font=helvetica, arial, sans-serif] Other Performance Aspects  CASE STUDIES  CDH4 Enhancements [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Name Node High – Availability[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Name Node federation[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Fencing  MapReduce Version - 2 [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif]HADOOP ANALYST [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Hive [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Hive concepts  Hive architecture[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Install and configure hive on cluster[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Different type of tables in hive  Hive library functions[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Buckets[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Partitions[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Joins in hive[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Inner joins  Outer Joins[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Hive UDF  PIG [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif]  Pig basics  Install and configure PIG on a cluster  PIG Library functions[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Pig Vs Hive  Write sample Pig Latin scripts  Modes of running PIG[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Running in Grunt shell  Running as Java program  PIG UDFs  Pig Macros[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Debugging PIG  IMPALA[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Difference between Impala Hive and Pig[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] How Impala gives good performance[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Exclusive features of Impala[/font][/color]

[color=#282828][font=helvetica, arial, sans-serif] Impala Challenges  Use cases of Impala [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif]NOSQL [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] HBase [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif]  HBase concepts  HBase architecture  Region server architecture[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] File storage architecture  HBase basics  Column access  Scans[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] HBase use cases  Install and configure HBase on a multi node cluster [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Create database, Develop and run sample applications[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Access data stored in HBase using clients like Java, Python and Pearl[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Map Reduce client to access the HBase data[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] HBase and Hive Integration[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] HBase admin tasks  Defining Schema and basic operation.[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Cassandra Basics[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] MongoDB Basics [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif]Other EcoSystem Components [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Sqoop [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Install and configure Sqoop on cluster[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Connecting to RDBMS  Installing Mysql[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Import data from Oracle/Mysql to hive[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Export data to Oracle/Mysql[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Internal mechanism of import/export [/font][/color]

[color=#282828][font=helvetica, arial, sans-serif] Oozie [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Oozie architecture  XML file specifications[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Install and configuring Oozie and Apache[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Specifying Work flow  Action nodes  Control nodes[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Oozie job coordinator [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Flume, Chukwa, Avro, Scribe, Thrift [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Flume and Chukwa concepts [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Use cases of Thrift, Avro and scribe[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Install and configure flume on cluster [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Create a sample application to capture logs from Apache using flume  Hadoop Challenges [/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Hadoop disaster recovery[/font][/color]
[color=#282828][font=helvetica, arial, sans-serif] Hadoop suitable cases [/font][/color]

[b]HIGHLIGHTS
 100% CERTIFICATION ASSURANCE
 BIG DATA UNIVERSITY(IBM) CERTIFICATION FREE
 TECHNICAL SUPPORT
 INTERVIEW QUESTIONS
 SAMPLE RESUMES[/b][color=#282828][font=helvetica, arial, sans-serif] [/font][/color]

October 8, 2013