HADOOP & BIG DATA Training in Ahmedabad

Excellent Training from Expert HADOOP & BIG DATA Professionals Developers

At CoreXtrime Consulting Service, we provide excellent HADOOP & BIG DATA Training to our students to make them capable to develop any type of applications. Our highly experience training faculties are well-versed in providing effectual HADOOP & BIG DATA training from starting to completion of training.

 

Leading Big Data & Hadoop training institute in Ahmedabad, Gujarat

Course Curriculum

S.No

Course Outline

Duration in Hours

1 Java Fundamentals 16
1.1 Basic java concepts (Object,Counstructor,Inheritance,Overriding)  
1.2 Multi-threading  
1.3 File I/O –Java. IO  
1.4 Collections –Java.Util.*, Java.Math, Java.Lang  
1.6 Java Serialization  
1.7 Eclipse IDE – Java Development.  
     
2 Hadoop Fundamentals 8
2.1 What is Big Data? Why Big Data?  
2.2 Hadoop Architecture & Components  
2.4 Hadoop Processing – Map Reduce, Spark Frameworks  
     
3 Hadoop Architecture And HDFS 4
3.1 Hadoop1.x (HDFS Basis)  
3.2 Hadoop2.x (YARN , Federation )  
3.3 File Storage  
3.4 Fault Tolerance, High Availablity  
3.5 Hadoop Configuration File  
3.6 Single Node Cluster / Multinode Cluster  
3.7 HDFS Commands  
     
4 Map Reduce 9
4.1 What Is MapReduce?  
4.2 Basic MapReduce Concepts  
4.3 Hadoop 2.x MapReduce Architecture and Components  
4.4 Input Splits , Relation Between Input Splits and HDFS Blocks  
4.5 Concepts of Mappers, Reducers, Combiners and Paritioning  
4.6 Demo (Word Count , Weather DataSet)  
  Advance MapReduce  
4.7 Counters , Distributed Cache  
4.8 MapSide Join , Reduce Side Join  
4.9  Inputs and Output formats to MR Program  
4.1 MRUnit  
     
5 Spark 10
5.1  What Is Spark?  
5.2  Basic Spark Concepts  
5.3  How Spark differs from Map Reduce?  
5.4 Spark Ecosystem  
5.5 Working With RDD  
5.6 Spark SQl  
5.7 Saprk Streaming  
5.8 Spark GraphX  
5.6 SparkMlib  
6 Hive 10
6.1  What is Hive, why we need it and its importance?  
6.2  How Hive is different from Traditional RDBMS  
6.3 Metastore in Hive  
6.4  Hive Data Types , Modeling in Hive, creating Hive structures and data load process.  
6.5  Concepts of Blocks, Hashing, External Tables etc.  
6.6  Concepts of serialization, deserialization  
6.7  Different Hive data storage formats including ORC, RC  
6.8  Introduction ton HiveQL and examples.  
6.9 Joining Table  
6.1 Concepts of Partitioning, Bucketing, Indexing  
6.11  Hive as an ELT tool and difference between Pig and Hive  
6.12  Writing and mastering Hive UDFs and Thrift Server  
     
7 Pig and Latin 12
7.1  Basics of Pig and Why Pig?  
7.2  Grunt  
7.3  Pig’s Data Model (Data Types)  
7.4 Apache Pig Architecture  
7.5 Installation  
7.6 Relational Operators (Group Operator, COGROUP Operator, Joins , Union, Diagnostic Operators)  
7.7  Writing Evaluation (Programming Structure in Pig)  
7.8  Filter  
7.9  Load & Store Functions  
7.1  Benefits of Pig over SQL language  
7.11 Built In Functions  
7.12 Execution of xml file using Pig  
     
8 HBase 12
8.1 Introduction to NoSQL  
8.2  HBase – Introduction (HBASE v/s RDBMS )  
8.3  When to use HBase  
8.4  Hbase Architecture  
8.5  HBase Families & Components  
8.6  HBase Data Model  
8.7  Data Storage and Distribution  
8.8 Zookeeper  
8.9  HBase Master  
     
9 Oozie 3
9.1 Flume and Sqoop Demo  
9.2  Oozie Components, Oozie Workflow,  
9.3  Scheduling with Oozie, Demo on Oozie Workflow,  
9.4 Oozie for MapReduce, PIG, Hive, and Sqoop  
9.5 Hadoop Project Demo,  
     
     
  Projects in Hadoop will also cover  

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