HADOOP Online Training
Introduction to HADOOP Training The Apache Hadoop is an open source software and highly scalable distributed platform enables applications to run on thousands of nodes involving thousands of terabytes of data. It can efficiently process large volume of unstructured data in relatively less time. MapReduce decomposes the data into map to reduce tasks and schedules for remote execution on data nodes. Big Data Hadoop Training curriculum is designed to briefly explain the concepts of HDFS and map reduce and also teach how to write map reduce programs and Implementing in HBase. Assimilate to write Hive and pig scripts. Inherit the advantages of Hadoop sqoop, how to run scripts to transfer data between Hadoop and RDBMS.
Course Curriculum Unit 1: Hadoop Basics Topics -The Motivation for Hadoop Training, Problems with traditional large-scale systems, Data Storage literature survey, Data Processing, literature Survey, Network Constraints, Requirements for a new approach, Hadoop: Basic Concepts, What is Hadoop?, The Hadoop, Distributed File System, Hadoop Map Reduce Works, Anatomy of a Hadoop Cluster, Hadoop demons, Master Daemons, Name node, Job Tracker, Secondary name node, Slave Daemons, Job tracker,Task tracker Unit 2: HDFS(Hadoop Distributed File System)
Topics Blocks and Splits, Input Splits, HDFS Splits, Data Replication, Hadoop Rack Aware, Data high availability, Cluster architecture and block placement. Unit 3: Programming Practices & Performance Tuning Topics - Developing MapReduce Programs in Local Mode, Running without HDFS, Pseudo-distributed Mode, Running all daemons in a single node, Fully distributed mode, Running daemons on dedicated nodes. Unit 4: Impala Topics - Difference between Impala Hive and Pig,How Impala gives good performance,Exclusive features of Impala,Impala Challenges,Use cases of Impala.
Unit 5: Hbase Topics - HBase concepts, HBase architecture, HBase basics, Region server architecture, File storage architecture, Column access, Scans, HBase use cases, Install and configure HBase on a multi node cluster, Create database, Develop and run sample applications, Access data stored in HBase using clients like Java, Python and Pearl, Map Reduce client to access the HBase data, HBase and Hive Integration, HBase admin tasks, Defining Schema and basic operation., Cassandra Basics, MongoDB Basics. Unit 6: Other EcoSystem Components –Sqoop Topics - Install and configure Sqoop on cluster, Connecting to RDBMS, Installing Mysql, Import data from Oracle/Mysql to hive, Export data to Oracle/Mysql, Internal mechanism of import/export. Unit 7: Flume, Chukwa, Avro, Scribe, Thrift Topics - Flume and Chukwa concepts, Use cases of Thrift, Avro and scribe, Install and configure flume on cluster, Create a sample application to capture logs from Apache using flume. Unit 8: Hadoop Challenges Topics - Hadoop disaster recovery, Hadoop suitable cases.
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