HADOOP on 27th Sep 11:00AM at Marthahalli (Bangalore) by Mr. Anil Kumar

Syllabus:

HADOOP DEVOLOPMENT • Introduction What is Cloud Computing What is Grid Computing What is Virtualization How above three are inter-related to each other What is Big Data Introduction to Analytics and the need for big data analytics Hadoop Solutions - Big Picture Hadoop distributions Comparing Hadoop Vs. Traditional systems Volunteer Computing Data Retrieval - Radom Access Vs. Sequential Access NoSQL Databases • The Motivation for Hadoop Problems with traditional large-scale systems Requirements for a new approach • Hadoop: Basic Concepts What is Hadoop? The Hadoop Distributed File System How MapReduce Works Anatomy of a Hadoop Cluster • Hadoop demons Namenode Datanode Secondary namenode Job tracker Task tracker • HDFS at detail Blocks and Splits Replication Data high availability Data Integrity Cluster architecture and block placement • Programming Practices & Performance Tuning Developing MapReduce Programs in Local Mode Pseudo-distributed Mode Fully distributed mode • Writing a MapReduce Program Examining a Sample MapReduce Program Basic API Concepts The Driver Code The Mapper The Reducer Hadoop's Streaming API • Setup Hadoop cluster of Apache, Cloudera and HortonWorks Install and configure Apache Hadoop Make a fully distributed Hadoop cluster on a single laptop/desktop Install and configure Cloudera Hadoop distribution in fully distributed mode Install and configure HortonWorks Hadoop distribution in fully distributed mode Monitoring the cluster Getting used to management console of Cloudera and Horton Works • Delving Deeper Into the Hadoop API Using Combiners The configure and close Methods SequenceFiles Partitioners Counters Directly Accessing HDFS ToolRunner Using The Distributed Cache • Common MapReduce Algorithms Sorting and Searching Indexing Classification/Machine Learning Term Frequency - Inverse Document Frequency Word Co-Occurrence Hands-On Exercise: Creating an Inverted Index • Debugging MapReduce Programs Testing with MRUnit Logging Other Debugging Strategies • Advanced MapReduce Programming A Recap of the MapReduce Flow Custom Writables and WritableComparables The Secondary Sort Creating InputFormats and OutputFormats Pipelining Jobs With Oozie Map-Side Joins Reduce-Side Joins • Monitoring and debugging on a Production Cluster Counters Skipping Bad Records Rerunning failed tasks with Isolation Runner • Tuning for Performance Reducing network traffic with combiner Reducing the amount of input data Using Compression Running with speculative execution Refactoring code and rewriting algorithms Parameters affecting Performance Other Performance Aspects Hadoop Ecosystem covered as part of Hadoop Developer • Eco system component: Hive Hive concepts Install and configure hive on cluster Create database, access it console Develop and run sample applications in Java/Python to access hive • Eco system component: Sqoop Install and configure sqoop on cluster Import data from Oracle/Mysql to hive • Eco system component: PIG Install and configure PIG Write sample Pig Latin scripts • Eco system component: HBase Hbase concepts Install and configure hbase on cluster Create database, Develop and run sample applications • Eco system component:Cassandra Cassandra concepts Install and configure Cassandra Create database, access it console Develop and run sample applications in Java/Python to access Cassandra data • Eco system component:Oozie Oozie concepts Install and configure Oozie on cluster Create a sample workflows and run them on cluster • Overview of other Eco system component: o Avro, Thrift, Rest, Mahout, Flume, Chukwa, YARN, MR2 etc. • Analytics Basics Analytics and big data analytics Commonly used analytics algorithms Analytics tools like R and Weka Mahout