training4u Posted October 9, 2013 Report Posted October 9, 2013 [color=#282828][size=4] [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] [b][color=#800080][size=6]Hadoop(Development + Admin+ Analytics) Regular class start on oct 11th 6 AM (IST) [/size][/color][color=#800080][size=6]by certified trainer.[/size][/color][/b] [b]HADOOP BASICS[/b] [font=helvetica, arial, sans-serif] The Motivation for Hadoop [/font] [font=helvetica, arial, sans-serif] Problems with traditional large-scale systems[/font] [font=helvetica, arial, sans-serif] Data Storage literature survey Data Processing literature Survey[/font] [font=helvetica, arial, sans-serif] Network Constraints Requirements for a new approach [/font] [font=helvetica, arial, sans-serif] Hadoop: Basic Concepts [/font] [font=helvetica, arial, sans-serif] What is Hadoop? [/font] [font=helvetica, arial, sans-serif] The Hadoop Distributed File System[/font] [font=helvetica, arial, sans-serif] Hadoop Map Reduce Works [/font] [font=helvetica, arial, sans-serif] Anatomy of a Hadoop Cluster[/font] [font=helvetica, arial, sans-serif] Hadoop demons [/font] [font=helvetica, arial, sans-serif] Master Daemons[/font] [font=helvetica, arial, sans-serif] Name node[/font] [font=helvetica, arial, sans-serif] Job Tracker[/font] [font=helvetica, arial, sans-serif] Secondary name node[/font] [font=helvetica, arial, sans-serif] Slave Daemons[/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] Blocks and Splits[/font] [font=helvetica, arial, sans-serif] Input Splits[/font] [font=helvetica, arial, sans-serif] HDFS Splits[/font] [font=helvetica, arial, sans-serif] Data Replication[/font] [font=helvetica, arial, sans-serif] Hadoop Rack Aware[/font] [font=helvetica, arial, sans-serif] Data high availability [/font] [font=helvetica, arial, sans-serif] Cluster architecture and block placement [/font] [font=helvetica, arial, sans-serif] CASE STUDIES Programming Practices & Performance Tuning [/font] [font=helvetica, arial, sans-serif] Developing MapReduce Programs in[/font] [font=helvetica, arial, sans-serif] Local Mode Running without HDFS[/font] [font=helvetica, arial, sans-serif] Pseudo-distributed Mode Running all daemons in a single node[/font] [font=helvetica, arial, sans-serif] Fully distributed mode Running daemons on dedicated nodes [/font] [font=helvetica, arial, sans-serif]Hadoop Administration [/font] [font=helvetica, arial, sans-serif] Setup Hadoop cluster of Apache, Cloudera, Hortonworks, Greenplum [/font] [font=helvetica, arial, sans-serif] Make a fully distributed Hadoop cluster on a single laptop/desktop[/font] [font=helvetica, arial, sans-serif] Install and configure Apache Hadoop on a multi node cluster in lab.[/font] [font=helvetica, arial, sans-serif] Install and configure Cloudera Hadoop distribution in fully distributed mode[/font] [font=helvetica, arial, sans-serif] Install and configure Horton Works Hadoop distribution in fully distributed mode[/font] [font=helvetica, arial, sans-serif] Install and configure Green Plum distribution in fully distributed mode[/font] [font=helvetica, arial, sans-serif] Monitoring the cluster [/font] [font=helvetica, arial, sans-serif] Getting used to management console of Cloudera and Horton Works[/font] [font=helvetica, arial, sans-serif] Name Node in Safe mode[/font] [font=helvetica, arial, sans-serif] Meta Data Backup[/font] [font=helvetica, arial, sans-serif] Ganglia and Nagios – Cluster monitoring[/font] [font=helvetica, arial, sans-serif] CASE STUDIES [/font] [font=helvetica, arial, sans-serif]Hadoop Development [/font] [font=helvetica, arial, sans-serif] Writing a MapReduce Program[/font] [font=helvetica, arial, sans-serif] Examining a Sample MapReduce Program[/font] [font=helvetica, arial, sans-serif] With several examples[/font] [font=helvetica, arial, sans-serif] Basic API Concepts [/font] [font=helvetica, arial, sans-serif] The Driver Code [/font] [font=helvetica, arial, sans-serif] The Mapper [/font] [font=helvetica, arial, sans-serif] The Reducer [/font] [font=helvetica, arial, sans-serif] Hadoop's Streaming API[/font] [font=helvetica, arial, sans-serif] Performing several Hadoop jobs [/font] [font=helvetica, arial, sans-serif] The configure and close Methods [/font] [font=helvetica, arial, sans-serif] Sequence Files[/font] [font=helvetica, arial, sans-serif] Record Reader[/font] [font=helvetica, arial, sans-serif] Record Writer[/font] [font=helvetica, arial, sans-serif] Role of Reporter[/font] [font=helvetica, arial, sans-serif] Output Collector Counters[/font] [font=helvetica, arial, sans-serif] Directly Accessing HDFS[/font] [font=helvetica, arial, sans-serif] ToolRunner[/font] [font=helvetica, arial, sans-serif] Using The Distributed Cache[/font] [font=helvetica, arial, sans-serif] Several MapReduce jobs (In Detailed) [/font] [font=helvetica, arial, sans-serif] MOST EFFECTIVE SEARCH USING MAPREDUCE[/font] [font=helvetica, arial, sans-serif] GENERATING THE RECOMMENDATIONS USING MAPREDUCE[/font] [font=helvetica, arial, sans-serif] PROCESSING THE LOG FILES USING MAPREDUCE[/font] [font=helvetica, arial, sans-serif] Identity Mapper [/font] [font=helvetica, arial, sans-serif] Identity Reducer[/font] [font=helvetica, arial, sans-serif] Exploring well known problems using MapReduce applications[/font] [font=helvetica, arial, sans-serif] Debugging MapReduce Programs [/font] [font=helvetica, arial, sans-serif] Testing with MRUnit[/font] [font=helvetica, arial, sans-serif] Logging [/font] [font=helvetica, arial, sans-serif] Other Debugging Strategies[/font] [font=helvetica, arial, sans-serif]. Advanced MapReduce Programming [/font] [font=helvetica, arial, sans-serif] The Secondary Sort Customized Input Formats and Output Formats[/font] [font=helvetica, arial, sans-serif] Joins in MapReduce Monitoring and debugging on a Production Cluster [/font] [font=helvetica, arial, sans-serif] Counters Skipping Bad Records Running in local mode[/font] [font=helvetica, arial, sans-serif] Tuning for Performance in MapReduce [/font] [font=helvetica, arial, sans-serif] Reducing network traffic with combiner Partitioners Reducing the amount of input data [/font] [font=helvetica, arial, sans-serif] Using Compression Reusing the JVM Running with speculative execution [/font] [font=helvetica, arial, sans-serif] Other Performance Aspects CASE STUDIES CDH4 Enhancements [/font] [font=helvetica, arial, sans-serif] Name Node High – Availability[/font] [font=helvetica, arial, sans-serif] Name Node federation[/font] [font=helvetica, arial, sans-serif] Fencing MapReduce Version - 2 [/font] [font=helvetica, arial, sans-serif]HADOOP ANALYST [/font] [font=helvetica, arial, sans-serif] Hive [/font] [font=helvetica, arial, sans-serif] Hive concepts Hive architecture[/font] [font=helvetica, arial, sans-serif] Install and configure hive on cluster[/font] [font=helvetica, arial, sans-serif] Different type of tables in hive Hive library functions[/font] [font=helvetica, arial, sans-serif] Buckets[/font] [font=helvetica, arial, sans-serif] Partitions[/font] [font=helvetica, arial, sans-serif] Joins in hive[/font] [font=helvetica, arial, sans-serif] Inner joins Outer Joins[/font] [font=helvetica, arial, sans-serif] Hive UDF PIG [/font] [font=helvetica, arial, sans-serif] Pig basics Install and configure PIG on a cluster PIG Library functions[/font] [font=helvetica, arial, sans-serif] Pig Vs Hive Write sample Pig Latin scripts Modes of running PIG[/font] [font=helvetica, arial, sans-serif] Running in Grunt shell Running as Java program PIG UDFs Pig Macros[/font] [font=helvetica, arial, sans-serif] Debugging PIG IMPALA[/font] [font=helvetica, arial, sans-serif] Difference between Impala Hive and Pig[/font] [font=helvetica, arial, sans-serif] How Impala gives good performance[/font] [font=helvetica, arial, sans-serif] Exclusive features of Impala[/font] [font=helvetica, arial, sans-serif] Impala Challenges Use cases of Impala [/font] [font=helvetica, arial, sans-serif]NOSQL [/font] [font=helvetica, arial, sans-serif] HBase [/font] [font=helvetica, arial, sans-serif] HBase concepts HBase architecture Region server architecture[/font] [font=helvetica, arial, sans-serif] File storage architecture HBase basics Column access Scans[/font] [font=helvetica, arial, sans-serif] HBase use cases Install and configure HBase on a multi node cluster [/font] [font=helvetica, arial, sans-serif] Create database, Develop and run sample applications[/font] [font=helvetica, arial, sans-serif] Access data stored in HBase using clients like Java, Python and Pearl[/font] [font=helvetica, arial, sans-serif] Map Reduce client to access the HBase data[/font] [font=helvetica, arial, sans-serif] HBase and Hive Integration[/font] [font=helvetica, arial, sans-serif] HBase admin tasks Defining Schema and basic operation.[/font] [font=helvetica, arial, sans-serif] Cassandra Basics[/font] [font=helvetica, arial, sans-serif] MongoDB Basics [/font] [font=helvetica, arial, sans-serif]Other EcoSystem Components [/font] [font=helvetica, arial, sans-serif] Sqoop [/font] [font=helvetica, arial, sans-serif] Install and configure Sqoop on cluster[/font] [font=helvetica, arial, sans-serif] Connecting to RDBMS Installing Mysql[/font] [font=helvetica, arial, sans-serif] Import data from Oracle/Mysql to hive[/font] [font=helvetica, arial, sans-serif] Export data to Oracle/Mysql[/font] [font=helvetica, arial, sans-serif] Internal mechanism of import/export [/font] [font=helvetica, arial, sans-serif] Oozie [/font] [font=helvetica, arial, sans-serif] Oozie architecture XML file specifications[/font] [font=helvetica, arial, sans-serif] Install and configuring Oozie and Apache[/font] [font=helvetica, arial, sans-serif] Specifying Work flow Action nodes Control nodes[/font] [font=helvetica, arial, sans-serif] Oozie job coordinator [/font] [font=helvetica, arial, sans-serif] Flume, Chukwa, Avro, Scribe, Thrift [/font] [font=helvetica, arial, sans-serif] Flume and Chukwa concepts [/font] [font=helvetica, arial, sans-serif] Use cases of Thrift, Avro and scribe[/font] [font=helvetica, arial, sans-serif] Install and configure flume on cluster [/font] [font=helvetica, arial, sans-serif] Create a sample application to capture logs from Apache using flume Hadoop Challenges [/font] [font=helvetica, arial, sans-serif] Hadoop disaster recovery[/font] [font=helvetica, arial, sans-serif] Hadoop suitable cases [/font] [b]HIGHLIGHTS 100% CERTIFICATION ASSURANCE BIG DATA UNIVERSITY(IBM) CERTIFICATION FREE TECHNICAL SUPPORT INTERVIEW QUESTIONS SAMPLE RESUMES[/b][font=helvetica, 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