CORE JAVA + BIGDATA COMBO Training in Pune/ Online

Courses Included:-

COREJAVA + HADOOP DEVELOPMENT + SPARK & SCALA

Duration of Training  : 4 months

Batch type  :  Weekdays/Weekends

Mode of Training  :  Classroom/Online/Corporate Training

CORE JAVA

What is Core Java?

It lays the foundation in terms of rich concepts and unique style of implementation, setting a benchmark in the industry.

Being an open-source, many technologies like Selenium, Hadoop, Sales Force and Data analytics have based their implementation on Java.• It is basically used to build stand-alone applications.

COURSE CONTENT

Introduction To Java 

History of Java

• What is Java , Java Flavors, characteristics
• JVM Architecture
• Bytecode
• Class Loader
• Unicode
• Classpath
• Path

Fundamentals of Java Programming 

• Obect Oriented concepts (OOP)
• Keywords, Datatypes, Variables, Operators, Casting
• Selection statement (if, switch)
• Control statements (while, do while, for) 
• Conditional statements (if, else, elseif, ? 🙂 )
• Static
• Arrays

Object Oriented Programming with Java 

• Classes and Objects
• Structure of a class – its internals (Data Members, methods)
• Using static
• Constructor
• this keyword
• modifiers
• playing with the object (copying, casting)
• Garbage collection
• Abstract class

Inheritance 

• Basics (extends keyword)
• Modifiers and their scope
• Deriving a class
• super, final keyword
• why java does not support multiple inheritance?

Polymorphism 

• overloading a method
• overloading a constructor
• method overriding
• accessing base class method 

Packages and Interfaces 

• basics
• modifiers and their scope chart
• setting classpath
• compiling and accessing a packaged class
• types of packages
• user defined package

Exploring java.lang package 

• String, StringBuffer, Arrays
• Wrapper classes 

Exception Handling 

• Basics
• Hierarchy of exceptions
• Handling exception – Try, catch, finally, throw, throws
• User defined exceptions

Threads 

• Basics, Thread class, Runnable Interface
• Thread model
• Life cycle – start(), run()
• Scheduling
• Deadlocks/Concurrency issues
• Synchronization – as a block, as a modifier
• Daemon thread

I/O Streams 

• Introduction
• Hierarchy of streams
• IO Stream, Byte Stream, Character Streams
• BufferedInputStream, BufferedOutputStream
• Reader and Writer class
• BufferedReader, PrintWriter
• Serialization 

Collection Framework 

• basics, hierarchy
• legacy classes – Vector, Queue, Stack, Enumeration, Dictionary, Properties
• List, ArrayList, LinkedList
• Set, HashSet, TreeSet
• Map, HashMap, TreeMap
• Generics
• Annotations
• Boxing/Unboxing
• Enums

Introduction to functional style of programming 

Mini application – discussion/implementation 

Tools

• Javadoc
• Javap
• Jar

IDE Tools

• Eclipse
• Myeclipse

Highlights of Training

• Industry experienced Professional
• Hands-on experience with Project orientation
• Interview based Questions

Our Approach

• Proper Communication with the student
• Course Ends With One Live Project
• Test on Each Topics
• Certification Overview
• Interview and resume preparation
• Discussion on Real Time Scenarios

 

HADOOP DEV + SPARK & SCALA

Why Hadoop?

• Solution for BigData Problem
• Open Source Technology
• Based on open source platforms
• Contains several tool for entire ETL data processing Framework
• It can process Distributed data and no need to store entire data in centralized storage as it is required for SQL based tools.  

COURSE CONTENT :

HADOOP DEV + SPARK & SCALA + NoSQL + Splunk + HDFS (Storage) + YARN (Hadoop Processing Framework) + MapReduce using Java (Processing Data) +  Apache Hive + Apache Pig + HBASE (Real NoSQL ) + Sqoop + Flume + Oozie  + Kafka With ZooKeeper + Cassandra + MongoDB + Apache Splunk

Big Data :

Distributed computing

Data management – Industry Challenges

Overview of Big Data

Characteristics of Big Data

Types of data

Sources of Big Data

Big Data examples

What is streaming data?

Batch vs Streaming data processing

Overview of Analytics

Big data Hadoop opportunities

Hadoop :                                       

Why we need Hadoop

Data centers and Hadoop Cluster overview

Overview of Hadoop Daemons

Hadoop Cluster and Racks

Learning Linux required for Hadoop

Hadoop ecosystem tools overview

Understanding the Hadoop configurations and Installation

HDFS (Storage) :

HDFS

HDFS Daemons – Namenode, Datanode, Secondary Namenode

Hadoop FS and Processing Environment’s UIs

Fault Tolerant 

High Availability

Block Replication

How to read and write files

Hadoop FS shell commands

YARN (Hadoop Processing Framework) :

YARN

YARN Daemons – Resource Manager, Node Manager etc.

Job assignment & Execution flow

MapReduce using Java (Processing Data) :

The introduction of MapReduce.

MapReduce Architecture

Data flow in MapReduce

Understand Difference Between Block and InputSplit

Role of RecordReader

Basic Configuration of MapReduce

MapReduce life cycle

How MapReduce Works

Writing and Executing the Basic MapReduce Program using Java

Submission & Initialization of MapReduce Job.

File Input/Output Formats in MapReduce Jobs

Text Input Format

Key Value Input Format

Sequence File Input Format

NLine Input Format

Joins

Map-side Joins

Reducer-side Joins

Word Count Example(or) Election Vote Count

Will cover five to Ten Map Reduce Examples with real time data

 Apache Hive :

Data warehouse basics

OLTP vs OLAP Concepts

Hive

Hive Architecture

Metastore DB and Metastore Service

Hive Query Language (HQL)

Managed and External Tables

Partitioning & Bucketing

Query Optimization

Hiveserver2 (Thrift server)

JDBC, ODBC connection to Hive

Hive Transactions

Hive UDFs

Working with Avro Schema and AVRO file format

Hands on Multiple Real Time datasets

Apache Pig :

Apache Pig

Advantage of Pig over MapReduce

Pig Latin (Scripting language for Pig)

Schema and Schema-less data in Pig

Structured , Semi-Structure data processing in Pig

Pig UDFs

HCatalog

Pig vs Hive Use case

Hands On Two more examples daily use case data analysis in google. And Analysis on Date time dataset

HBASE (Real NoSQL) :

Introduction to HBASE

Basic Configurations of HBASE

Fundamentals of HBase

What is NoSQL?

HBase Data Model

Table and Row.

Column Family and Column Qualifier.

Cell and its Versioning

Categories of NoSQL Data Bases

Key-Value Database

Document Database

Column Family Database

HBASE Architecture

HMaster

Region Servers

Regions

MemStore

Store

SQL vs. NOSQL

How HBASE is differed from RDBMS

HDFS vs. HBase

Client-side buffering or bulk uploads

HBase Designing Tables

HBase Operations

Get

Scan

Put

Delete

Live Dataset

Sqoop :

Sqoop commands

Sqoop practical implementation 

Importing data to HDFS

Importing data to Hive

Exporting data to RDBMS

Sqoop connectors

Flume :

Flume commands

Configuration of Source, Channel and Sink

Fan-out flume agents

How to load data in Hadoop that is coming from web server or other storage

How to load streaming data from Twitter data in HDFS using Hadoop

Oozie :

Oozie

Action Node and Control Flow node

Designing workflow jobs

How to schedule jobs using Oozie

How to schedule jobs which are time based

Oozie Conf file

Scala :

Scala 

Syntax formation, Datatypes , Variables

Classes and Objects

Basic Types and Operations

Functional Objects

Built-in Control Structures

Functions and Closures

Composition and Inheritance

Scala’s Hierarchy

Traits

Packages and Imports

Working with Lists, Collections

Abstract Members

Implicit Conversions and Parameters

For Expressions Revisited

The Scala Collections API

Extractors

Modular Programming Using Objects

Spark :

Spark

Architecture and Spark APIs

Spark components 

Spark master

Driver

Executor

Worker

Significance of Spark context

Concept of Resilient distributed datasets (RDDs)

Properties of RDD

Creating RDDs

Transformations in RDD

Actions in RDD

Saving data through RDD

Key-value pair RDD

Invoking Spark shell

Loading a file in shell

Performing some basic operations on files in Spark shell

Spark application overview

Job scheduling process

DAG scheduler

RDD graph and lineage

Life cycle of spark application

How to choose between the different persistence levels for caching RDDs

Submit in cluster mode

Web UI – application monitoring

Important spark configuration properties

Spark SQL overview

Spark SQL demo

SchemaRDD and data frames

Joining, Filtering and Sorting Dataset

Spark SQL example program demo and code walk through

Kafka With ZooKeeper :

What is Kafka

Cluster architecture With Hands On

Basic operation

Integration with spark

Integration with Camel

Additional Configuration

Security and Authentication

Apache Kafka With Spring Boot Integration

Running 

Usecase

Apache Splunk :

Introduction & Installing Splunk

Play with Data and Feed the Data

Searching & Reporting

Visualizing Your Data

Advanced Splunk Concepts 

Cassandra + MongoDB :

Introduction of NoSQL 

What is NOSQL & N0-SQL Data Types

System Setup Process

MongoDB Introduction

MongoDB Installation 

DataBase Creation in MongoDB

ACID and CAP Theorum 

What is JSON and what all are JSON Features? 

JSON and XML Difference 

CRUD Operations – Create , Read, Update, Delete

Cassandra Introduction

Cassandra – Different Data Supports 

Cassandra – Architecture in Detail 

Cassandra’s SPOF & Replication Factor

Cassandra – Installation & Different Data Types

Database Creation in Cassandra 

Tables Creation in Cassandra 

Cassandra Database and Table Schema and Data 

Update, Delete, Insert Data in Cassandra Table 

Insert Data From File in Cassandra Table 

Add & Delete Columns in Cassandra Table 

Cassandra Collections