Hadoop Demo On 28Th Sept At 8.30 Pm(Ist)

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[color=#800080][font=helvetica, arial, sans-serif][size=6][b]Hadoop n[/b][/size][/font][/color][b][color=#800080][size=6]ew batch starts on 28th Sept At 8.30 PM(IST)[/size][/color][color=#800080][size=6]|| 28th Sept at 11 AM(EST) [/size][/color][/b]
[color=#008000][font=helvetica, arial, sans-serif][b][size=6]by 9+ yrs exp.trainers [/size][/b][size=6]with Real time Data Set [/size][/font][/color]

[font=helvetica, arial, sans-serif]Apache Hadoop Big Data Training Course[/font]
[font=helvetica, arial, sans-serif]Introduction to HADOOP[/font]
[font=helvetica, arial, sans-serif] Distributed computing , cloud computing[/font]
[font=helvetica, arial, sans-serif] Big data Basics and Need for Parallel Processing[/font]
[font=helvetica, arial, sans-serif] How Hadoop works ?[/font]
[font=helvetica, arial, sans-serif] Introduction to HDFS and Map Reduce[/font]
[font=helvetica, arial, sans-serif]Hadoop Architecture Details[/font]
[font=helvetica, arial, sans-serif] Name Node[/font]
[font=helvetica, arial, sans-serif] Data Node[/font]
[font=helvetica, arial, sans-serif] Secondary Name Node[/font]
[font=helvetica, arial, sans-serif] Job Tracker[/font]
[font=helvetica, arial, sans-serif] Task Tracker[/font]
[font=helvetica, arial, sans-serif]HDFS ( Hadoop - Distributed File System)[/font]
[font=helvetica, arial, sans-serif] Hadoop Distributed file system , Background, GFS[/font]
[font=helvetica, arial, sans-serif] Data Replication[/font]
[font=helvetica, arial, sans-serif] Data Storage[/font]
[font=helvetica, arial, sans-serif] Data Retrieval[/font]
[font=helvetica, arial, sans-serif] Additional HDFS commands[/font]
[font=helvetica, arial, sans-serif]MapReduce Programming[/font]
[font=helvetica, arial, sans-serif] MapReduce, Background[/font]
[font=helvetica, arial, sans-serif] Writing MapReduce Programs[/font]
[font=helvetica, arial, sans-serif] Writable and WritableComparable[/font]
[font=helvetica, arial, sans-serif] Input Format, Output Format[/font]
[font=helvetica, arial, sans-serif] Input Split and Block size[/font]
[font=helvetica, arial, sans-serif] Combiner[/font]
[font=helvetica, arial, sans-serif] Partitioner[/font]
[font=helvetica, arial, sans-serif] Number of Mappers and Reducers[/font]
[font=helvetica, arial, sans-serif] Counters[/font]
[font=helvetica, arial, sans-serif]Map Reduce Algorithms and Exercises[/font]
[font=helvetica, arial, sans-serif] Line Count and Word Count[/font]
[font=helvetica, arial, sans-serif] Distributed Search[/font]
[font=helvetica, arial, sans-serif] Sorting Data – Key Value Data Type Mathematical Transformation example[/font]
[font=helvetica, arial, sans-serif] Working with Counters exercise[/font]
[font=helvetica, arial, sans-serif] Distributed Cache exercise[/font]
[font=helvetica, arial, sans-serif] Zero Reducer based exercises[/font]
[font=helvetica, arial, sans-serif]Hadoop Streaming[/font]
[font=helvetica, arial, sans-serif] Introduction to Hadoop Streaming[/font]
[font=helvetica, arial, sans-serif] Streaming API details and use cases[/font]
[font=helvetica, arial, sans-serif] Python Based Example for Streaming API[/font]
[font=helvetica, arial, sans-serif] Exercise for Hadoop Streaming ( XML Files ) Based.[/font]
[font=helvetica, arial, sans-serif] Exercises on Ruby[/font]
[font=helvetica, arial, sans-serif] Exercise on C# using MS-Azure.[/font]
[font=helvetica, arial, sans-serif]Apache Pig[/font]
[font=helvetica, arial, sans-serif] Installation[/font]
[font=helvetica, arial, sans-serif] Execution Types[/font]
[font=helvetica, arial, sans-serif] Grunt Shell[/font]
[font=helvetica, arial, sans-serif] Pig Latin[/font]
[font=helvetica, arial, sans-serif] Data Processing[/font]
[font=helvetica, arial, sans-serif] Loading and Storing[/font]
[font=helvetica, arial, sans-serif] Data Filtering[/font]
[font=helvetica, arial, sans-serif] Grouping & Joining Operations[/font]
[font=helvetica, arial, sans-serif] Hands on Exercises[/font]
[font=helvetica, arial, sans-serif]Apache HBase Installation and Details[/font]
[font=helvetica, arial, sans-serif] HBase and NOSQL Introduction[/font]
[font=helvetica, arial, sans-serif] HBase Installation and Configuration.[/font]
[font=helvetica, arial, sans-serif] HBase and Java Based integration[/font]
[font=helvetica, arial, sans-serif] HBase Hadoop Integration Details.[/font]
[font=helvetica, arial, sans-serif] Hbase basic exercises[/font]
[font=helvetica, arial, sans-serif]Apache Hive Instalaltion and Details[/font]
[font=helvetica, arial, sans-serif] Hive Installation on Single cluster Hadoop Node.[/font]
[font=helvetica, arial, sans-serif] Hive Services[/font]
[font=helvetica, arial, sans-serif] Hive Shell Description[/font]
[font=helvetica, arial, sans-serif] Hive Server·[/font]
[font=helvetica, arial, sans-serif] Meta store Details[/font]
[font=helvetica, arial, sans-serif] Hive QL Basics Working with Tables, Databases etc.[/font]
[font=helvetica, arial, sans-serif] Hive JDBC programming[/font]
[font=helvetica, arial, sans-serif] Hands on Exercises and Assignments[/font]
[font=helvetica, arial, sans-serif]Introduction to Amazon Map Reduce (AWS-EMR)[/font]
[font=helvetica, arial, sans-serif] Hadoop using Amaozon Web Service[/font]
[font=helvetica, arial, sans-serif] AWS MapReduce and EC2[/font]
[font=helvetica, arial, sans-serif] AWS - S3 Service Model.[/font]
[font=helvetica, arial, sans-serif] AWS-MR Architecture.[/font]
[font=helvetica, arial, sans-serif] Streaming Exercise using EMR JobFlow.[/font]
[font=helvetica, arial, sans-serif]Hadoop Infrastructure Planning[/font]
[font=helvetica, arial, sans-serif] Basic Hadoop hardware and software req[/font]
[font=helvetica, arial, sans-serif] Small , Medium and Large cluster[/font]
[font=helvetica, arial, sans-serif] Networking challenges in Hadoop Deployment[/font]
[font=helvetica, arial, sans-serif] Disaster Recovery ( DR ) in Hadoop .[/font]
[font=helvetica, arial, sans-serif] Performance Tuning a large cluster[/font]
[font=helvetica, arial, sans-serif]Hadoop Industry Solutions[/font]
[font=helvetica, arial, sans-serif] EMC GreenPlum Introduction[/font]
[font=helvetica, arial, sans-serif] IBM BigInsight Details[/font]
[font=helvetica, arial, sans-serif] Oracle , Microsoft etc Hadoop Offerings[/font]
[font=helvetica, arial, sans-serif] Cloudera and HortonWorks Hadoop Package[/font]
[font=helvetica, arial, sans-serif]Hadoop and Cloud Computing[/font]
[font=helvetica, arial, sans-serif] Using Cloud technologies for distributed processing[/font]
[font=helvetica, arial, sans-serif] Hadoop on Amazon Web Service.[/font]
[font=helvetica, arial, sans-serif] Hadoop in Oracle Cloud / RackSpace[/font]
[font=helvetica, arial, sans-serif]===================================[/font][/size][/color][list]
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[color=#000000][font=helvetica, arial, sans-serif][size=6][b]Email:[/b][/size][/font][/color][color=#FF0000][font=helvetica, arial, sans-serif][size=6][b] [email protected][/b][/size][/font][/color]

[color=#000000][font=helvetica, arial, sans-serif][size=6][b]Contact No[/b][/size][/font][/color][color=#FF0000][font=helvetica, arial, sans-serif][size=6][b]: [/b][/size][/font][/color][color=#FF0000][font=helvetica, arial, sans-serif][size=6][b]+91 9036 298 699 [/b][/size][/font][/color][color=#000000][font=helvetica, arial, sans-serif][size=6][b](or)[/b][/size][/font][/color][color=#FF0000][font=helvetica, arial, sans-serif][size=6][b] +91 78 29 29 7899[/b][/size][/font][/color]