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Mail Us:-sathyainhydgmail.com Call Us:- 65538958 /65538968 / 65538978 Hadoop Training Sathya technologies is one of the best Software Training Institute in Hyderabad india.offers world class training on various trending software technologies in hyderabad Join our hadoop Training Classes and Get trained By Real time Industry professionals to Get hands on experience. We provide hadoop classroom and Online trainings for students software and administration professionals. Our training sessions covers all information from basic to advanced level.we ensure that sathya tech is the best hadoop Training institute in hyderabad HADOOP TRAINING COURSE CONTENT MAP REDUCE  Map Reduce Architecture  Map Reduce Programing Model  Map Reduce Program structure  Hadoop streaming  Executing Java – Map Reduce Job  Understanding of Java Map Reduce Classes  Configuration  Path  Job  Mapper  Reducer  Text  Intwritables  Long writables  File Input Format  File Output Format  Generic Options Pavser Joining Datasets in Map Reduce Jobs  Map Joins  reduce Joins

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Combiners Partitioners  Python Map Reduce  Unit Testing Mapeduce Jobs  Hadoop Pipelining  Creating Input and Output Formats in Map Reduce Jobs  Text Input Format  Key Value Input Format  Sequence File Input Format  Data Localization in Map Reduce  Examples HIVE  Introduction  Hive Architecture  Hive Metastore  Hive Query Launguage  Difference between HQL and SQL  Hive Built in Functions  Hive UDF user defined functions  Hive UDAF user defined Aggregated functions  Hive UDTF user defined table Generated functions  Hive Serde  Hive Hbase Integration  Hive Working with unstructured data  Hive Working With Xml Data  Hive Working With Json Data  Hive Working With Urls And Weblog Data  Hive – Json – Serde  Loading Data From Local Files To Hive Tables  Loading Data From Hdfs Files To Hive Tables  Tables Types  Inner Tables  External Tables  Partitioned Tables  Non – Partitioned Tables  Dynamic Partitions In Hive  Concept Of Bucketing  Hive Views  Hive Unions  Hive Joins  Multi Table / File Inserts  Inserting Into Local Files  Inserting Into Hdfs Files  Array Operations In Hive Map Associative Arrays Operations in Hive

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 Hive UDF by Java  Hive UDF by Python PIG  Introduction to pig  Pig Latin Script  Pig Console / Grunt Shell  Execting Pig Latin Script  Pig Relations Bags Tuples Fields  Data Types  Nulls  Constants  Expressions  Schemas  Parameter Substitution  Arithmetic Operators  Comparison Operators  Null Operators  Boolean Operators  Defence Operators  Sign Operators  Flatten Operators  Caster Operators Relational Operators in Pig  ICOGROUP  CROSS  DISTINCT  FILTER  FOREACH  GROUP  JOIN INNER  JOIN OUTER  LIMIT  LOAD  ORDER  SAMPLE  SPILT  STORE  UNION Diagnostic Operators in Pig  Describe  Dump  Explain

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 Illustrate Eval Functions in Pig  AVG  CONCAT  COUNT  CONI-STAR  DIFF  IS EMPTY  MAX  MIN  SIZE  SUM  TOKENIZE  writing Custom UDFS in Pig  Using Java  Using Python SQOOP SQL + HADOOP  Introduction to Sqoop  SQOOP Import  SQOOP Export  Importing Data From RDBMS to HDFS  Importing Data From RDBMS to HIVE  Importing Data From RDBMS to HBASE  Exporting From HASE to RDBMS  Exporting From HBASE to RDBMS  Exporting From HIVE to RDBMS  Exporting From HDFS to RDBMS  Transformations While Importing / Exporting  Defining SQOOP Jobs NOSQL  What is “Not only SQL”  NOSQL Advantages  What is problem with RDBMS for Large  Data Scaling Systems  Types of NOSQL Purposes  Key Value Store  Columer Store  Document Store  Graph Store  Introduction to ricsk – NOSQL Database  Introduction to cassandra – NOSQL Database  Introduction to MangoDB and CouchDB Database  Introduction to Neo4j – NOSQL Database  Intergration of NOSQL Databases with Hadoop

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HBASE  Introduction to big table  What is NOSQL and colummer store Database  HBASE Introduction  Hbase use cases  Hbase basics  Column families  Scans  Hbase Architecture  Clients  Rest  Thrift  Java  Hive  Map Reduce Integration  Map Reduce Over Hbase  Hbase data Modeling  Hbase Schema design  Hbase CRUD operators  Hive Hbase interagation  Hbase storage handles OOZIE  Introduction to OOZIE  OOZIE as a seheduler  OOZIE as a Workflow designer  Seheduling jobs OOZIE CODE  Defining Dependences between jobs  OOZIE Code Examples  Conditionally controling jobs  OOZIE Code Examples  Defining parallel jobs OOZIE Code Examples FLUME  Introduction to FLUME  What is the streaming File  FLUME Architecture  FLUME Nodes FLUME Manager  FLUME Local Physical Node  FLUME Agents FLUME Collector ZOOKIPER  Introduction to ZOOKEEPER  ZOOKEEPER Architecture  Controlling Connection of Distbrited Apps  HBASE ZOOKEEPER

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 Flume ZOOKEEPER  A Sample Code Free On Hadoop Course  Phyton Pydoop  Mongo DB  Cascading Tags:- hadoop training institute in hydearbad hadoop training in ameerpet hadoop training classes in ameerpet hadoop training classes in hyderabad hadoop training in hyderabad