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

Java Fundamentals

  • Basic java concepts
  • Multi-threading
  • File I/O –Java. IO
  • Collections –Java.Util.*, Java.Math, Java.Lang
  • Java Serialization
  • Java Database Connectivity –JDBC
  • Java Unit testing Frameworks (Junit / TestNG)
  • Eclipse IDE – Java Development.

Hadoop Fundamentals

  • What is Big Data? Why Big Data?
  • Hadoop Architecture & Components
  • Hadoop Processing – Map Reduce, Spark Frameworks

Hadoop Architecture And HDFS

  • Hadoop1.x (HDFS Basis)
  • Hadoop2.x (YARN , Federation )
  • File Storage
  • Fault Tolerance, High Availablity
  • Hadoop Configuration File
  • Single Node Cluster / Multinode Cluster
  • HDFS Commands

Map Reduce

  • What Is MapReduce?
  • Basic MapReduce Concepts
  • Hadoop 2.x MapReduce Architecture and Components
  • Input Splits , Relation Between Input Splits and HDFS Blocks
  • Concepts of Mappers, Reducers, Combiners and Paritioning
  • Demo (Word Count , Weather DataSet)

Advance MapReduce

  • Counters , Distributed Cache
  • MapSide Join , Reduce Side Join
  • Inputs and Output formats to MR Program
  • MRUnit

Spark

  • What Is Spark?
  • Basic Spark Concepts
  • How Spark differs from Map Reduce?
  • Spark Ecosystem
  • Working With RDD

Map Reduce

  • What is Hive, why we need it and its importance?
  • How Hive is different from Traditional RDBMS
  • Metastore in Hive
  • Hive Data Types , Modeling in Hive, creating Hive structures and data load process.
  • Concepts of Blocks, Hashing, External Tables etc.
  • Concepts of serialization, deserialization
  • Different Hive data storage formats including ORC, RC
  • Introduction ton HiveQL and examples.
  • Joining Table
  • Concepts of Partitioning, Bucketing, Indexing
  • Hive as an ELT tool and difference between Pig and Hive
  • Writing and mastering Hive UDFs and Thrift Server

Pig and Latin

  • Basics of Pig and Why Pig?
  • Grunt
  • Pig’s Data Model (Data Types)
  • Apache Pig Architecture
  • Installation
  • Relational Operators (Group Operator, COGROUP Operator, Joins , Union, Diagnostic Operators)
  • Writing Evaluation (Programming Structure in Pig)
  • Filter
  • Load & Store Functions
  • Benefits of Pig over SQL language
  • Built In Functions
  • Execution of xml file using Pig

HBase

  • Introduction to NoSQL
  • HBase – Introduction (HBASE v/s RDBMS )
  • When to use HBase
  • Hbase Architecture
  • HBase Families & Components
  • HBase Data Model
  • Data Storage and Distribution
  • Zookeeper
  • HBase Master

Oozie

  • Flume and Sqoop Demo
  • Oozie Components, Oozie Workflow,
  • Scheduling with Oozie, Demo on Oozie Workflow,
  • Oozie for MapReduce, PIG, Hive, and Sqoop
  • Hadoop Project Demo,

Hadoop Security

  • Security Overview
  • Knox Exercise
  • Access Control Labels

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