Hadoop Online training from www. Imaginelife.in
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Learn Hadoop online training from www.imaginelife.in

Learn Hadoop online training from www.imaginelife.in

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Hadoop Online training from www. Imaginelife.in Presentation Transcript

  • 1. IT COURSES Online live classes www.imaginelife.in PH:8499068708 :8341832707 EMAIL:suport@imaginelife.in Hadoop
  • 2. Topics To Be covered in Hadoop course Introduction to Big Data & Hadoop 1.what is Big data? 2.what are the challenges for processing big data? 3.what is Hadoop? 4.way Hadoop? 5.History of Hadoop 6.Use cases of Hadoop 7.Hadoop eco System 8.HDFS 9.Mapreduce 10.Statistics
  • 3. Understanding the cluster 1.Typical workflow 2.Writing files to HDPS 3.Reading files from HDFS 4.Rack Awareness 5.5 Daemons 6.HDFS commands HANDS-on Installing the cluster 1.CDH4 Pseudo cluster 2.CDH4 Multi node cluster 3.configuration 4.cluster on EC2 cloud 5.How to use AWS EMR 6.Hands –on Exercises
  • 4. Routine Admin and Monitoring Activities 1.Meta Data and Data Backups 2.commissioning and Decommissioning nodes 3.Recover from Namenode Failure 4.Namenode High Availability 5.Monitoring using ganglia and Nagios Let’s talk MapReduce 1.Before MapReduce 2.MapReduce Overview 3.word count problem 4.word count flow and solution 5.MapReduce flow 6.Algorithms for simple problems 7.Algorithms for complex problems
  • 5. Developing the MapReduce Application 1.Data types 2.File Formats 3.Explain the Driver,Mapper and Reducer code 4. configuring Development environment-Eclipse 5.Writing Unit Test 6.Running locally 7.Hands –on exercises How Map Reduce Works 1.Anatomy of map Reduce job run 2.Job Submission 3.Job initialization 4.Task Assignment 5.Job completion
  • 6. 6.Job Scheduling 7.Job Failures 8.shuffle and sort 9.Oozie Workflows 10.Hands –on Exercises MapReduce Types and Formats 1.MapReduce Types 3.output Formats –text Output, binary output,multiple outputs 4.Lazy output and database output 5.Hands-on Exercises
  • 7. MapReduce Features 1.Counters 2.Joins-map side and Reduce Side 3.Sorting 4.MapReduce combiner 5.MapReduce partitioner 6.MapReduce Distributed Cache 7.Hands-on Exercises Hive 1. What is Hive? 2.what Hive is not? 3.Hive Architecture 4.SQL vs Hive QL 5.Data Types 6.Managed Tables and External Tables 7.partitions 8.Buckets
  • 8. 9.Storage formats 10.serDes 11.importing Data 12.Joins-map side and Reduce Side 13. UDFs 14.Hands-on Exercises Imapla 1.Need for RTQ 2.impala Overview 3.Impala Architecture 4.Hands-on Exercises
  • 9. Pig 1.What is Pig? Why Pig? 2.Running pig 3.Data 4.pig Latin Statements 5.Schemas 6.Validations 7.Functions and Macros 8.UDFs 9.When to Use pig and HIVE 10.Hands-on Exercises
  • 10. NoSQL and HBase 1.Why noSQL? 2.Problems with RDBMS 3.cap theorem 4.HBase Concepts 5.Use Cases for HBase 6.HBase Data Model 7.HBase Shell 8.HBase Architecture 9.Minor & major Compaction 10.Bloom Filter&Block cache 11.Schema Design 12.Hands-0n Exercises
  • 11. Sqoop 1.What is Sqoop? 2.Motivation 3.Sqoop Commands 4.Importing Data to HDFS HIVE HBase 5.Exposing Data 6.Sqoop Connectors 7.Hands on Exercises FLUME 1.What is Flume? 2.Use Cases 3.Flume Topology –Source,Channel and Sink 4.Hands-on Execises –Ingest Data from twitter and Analyze With Hive
  • 12. Machine Learning and Mahout 1.3Cs of Machine Learning 2.Introduction to Mahout 3.Hands-on Exercise:Build a Recommendation System using Mahout POCs 1.Banking Use case 2.Telecom Use case