Big data hadoop training

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The course is a comprehensive package for the individuals who want to gain insight into the basic and advanced concepts of Big Data and Hadoop. On completion of this course, the learners will be able to understand what goes beyond the processing of large data as well as industry switches from excel based analytics to real time analytics. The course focuses on improving the rudimentary knowledge of Big Data and Hadoop and further provides an overview of commercial distributions of Hadoop and components involving around Hadoop ecosystem.

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BIGDATA HADOOP – ANALYTIC Module 1: Introduction Big data and Hadoop  What is Big Data  Characteristics of big data  Big Data challenges  Popular tools used with big data For storing processing analysing visualization  Where Hadoop fits in  Traditional data analytics architecture versus Hadoop  What is Hadoop  History of Hadoop  Hadoop’s key characteristics  Hadoop usage Module 2: Hadoop Eco-system Architecture  Hadoop eco-system core components  HDFS architecture overview of MRv1  HDFS daemons  Files and blocks  Anatomy of a file write read  Replication rack awareness Module 3: Introduction to YARN  What is YARN  MR1 v MR2  YARN architecture  HDFS Federation  YARN Deamons  YARN Job execution workflow  Authentication and high availability in Hadoop Module 4: Hadoop Cluster Configuration  Hortonworks sandbox installation configuration  Hadoop Configuration files  Working with Hadoop services using Ambari  Hadoop deamons  Browsing Hadoop UI consoles  Basic Hadoop Shell commands  Eclipse winscp installation configurations on VM Module 5: Basics of MapReduce on YARN  Running a MapReduce application in MR2  MapReduce Framework on YARN  Fault tolerance in YARN  Map Reduce Shuffle phases  Understanding Mapper Reducer Driver classes Module 6: MapReduce Programming  Writing Map Reduce WordCount program  Executing monitoring a Map Reduce job  Use case - Sales calculation using M/R

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Module 7: Analysis using Pig  Background of Pig  Pig architecture  Pig Latin basics  Pig execution modes  Pig processing – loading and transforming data  Pig built-in functions  Filtering grouping sorting data  Relational join operators  Pig Scripting  Pig UDFs Module 8: Analysis using Hive Data Warehousing Infrastructure  Background of Hive  Hive architecture  Hive Query Language  Derby to MySQL database  Managed external tables  Data processing – loading data into tables  Hive Query Language  Using Hive built-in functions  Partitioning data using Hive  Bucketing data  Hive Scripting  Using Hive UDFs Module 9: Working with HBase  HBase overview  Data model  HBase architecture  HBase shell  Zookeeper its role in HBase environment  HBase Shell environment  Creating table  Creating column families  CLI commands – get put delete scan  Scan Filter operations Module 10: Importing Exporting Data using Sqoop  Importing data from RDBMS to HDFS  Exporting data from HDFS to RDBMS  Importing exporting data between RDBMS Hive tables Module 11: Oozie Workflow Management  Overview of Oozie  Oozie Workflow Architecture  Creating workflows with Oozie Module 12: Using Flume for Analysing Streaming Data  Introduction to Flume  Flume Architecture  Flume Demo

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203/RATNMANI BLDG DADA PATIL WADI OPP ICICI ATM THANE WEST Web: www.nettechindia.com Phone : 9870803004/ 9870803005

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