HADOOP DEV + SPARK & SCALA TRAINING IN KOCHI

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1880 Learners

  • 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.

Overview

BigData Hadoop Mode of Training: Classroom / Online / Corporate Training

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

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INTRO VIDEO

Why Radical Technologies

  • Highly practical oriented training
  • 25000+ Man-hours of Real-time projects & scenarios
  • 10 to 20+ year Experienced corporate trainers With Real Time Experience.
  • Building up professionals by highly experienced professionals
  • 100 % quality assurance in training .
  • 10000+ Placement Records and 180+ MNC’s and Consultancies Tie up

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Benefits

  • 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 Curriculum

Course description

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

Pre-requisites

It is a big myth that if a guy don’t know Java then he can’t learn Hadoop. The truth is that Only Map Reduce framework needs Java except Map Reduce all other components are based on different terms like Hive is similar to SQL, HBase is similar to RDBMS and Pig is script based.

Only MR requires Java but there are so many organizations who started hiring on specific skill set also like HBASE developer or Pig and Hive specific requirements. Knowing MapReuce also is just like become all-rounder in Hadoop for any requirement.

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

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

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

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

YARN Daemons – Resource Manager, NodeManager etc.

Job assignment & Execution flow

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.

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

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

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 commands

Sqoop practical implementation 

Importing data to HDFS

Importing data to Hive

Exporting data to RDBMS

Sqoop connectors

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

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 

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

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

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

Introduction & Installing Splunk

Play with Data and Feed the Data

Searching & Reporting

Visualizing Your Data

Advanced Splunk Concepts 

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

Training Options

Live Online Training

  • Highly practical oriented training
  • Installation of Software On your System
  • 24/7 Email and Phone Support
  • 100% Placement Assistance until you get placed
  • Global Certification Preparation
  • Trainer Student Interactive Portal
  • Assignments and Projects Guided by Mentors
  • And Many More Features

Course completion certificate and Global Certifications are part of our all Master Program

Live Classroom Training

  • Weekend / Weekdays / Morning / Evening Batches
  • 80:20 Practical and Theory Ratio
  • Real-life Case Studies
  • Easy Coverup if you missed any sessions
  • PSI | Kryterion | Redhat Test Centers
  • Life Time Video Classroom Access ( coming soon )
  • Resume Preparations and Mock Interviews
  • And Many More Features

Course completion certificate and Global Certifications are part of our all Master Program

Exam & Certification

Course Reviews

I had a wonderful experience in Radical technologies where i did training in Hadoop development under the guidance of Shanit Sir. He started from the very basic and covered and shared everything he knew in this field. He was brilliant and had a lot of experience in this field. We did hands on for every topic we covered, and that’s the most important thing because honestly theoretical knowledge cannot land you a job.
Rohit Agrawal Hadoop
I have recently completed Linux course under Anand Sir and can assuredly say that it is definitely the best Linux course in Pune. Since most of the Linux courses from other sources are strictly focused on clearing the certification, they will not provide an insight into real-world server administration, but that is not the case with Anand Sir’s course. Anand Sir being an experienced IT infrastructure professional has an excellent understanding of how a data center works and all these information is seamlessly integrated into his classes.
Manu Sunil Linux
I had undergone oracle DBA course under Chetan sir’s Guidance an it was a very good learning experience overall since they not only provide us with theoretical knowledge but also conduct lot of practical sessions which are really fruitful and also the way of teaching is very fine clear and crisp which is easier to understand , overall I had a great time for around 2 months , they really train you well.also make it a point to clear all your doubts and provide you with clear and in-depth concepts hence hope to join sometime again
Reema banerjee Oracle DBA
I have completed Oracle DBA 11g from Radical technology pune. Excellent trainer (chetna gupta ). The trainer kept the energy level up and kept us interested throughout. Very practical, hands on experience. Gave us real-time examples, excellent tips and hints. It was a great experience with Radical technologies.
Mrudul Bhokare Oracle DBA
Linux learning with Anand sir is truly different experience… I don’t have any idea about Linux and system but Anand sir taught with scratch…He has a great knowledge and the best trainer…he can solve all your queries related to Linux in very simple way and giving nice examples… 100 🌟 to Anand Sir.
Harsh Singh Parihar Linux
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