Radical Technologies
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  • 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.
Satisfied Learners
One time class room registraion to click here Fee 1000/-

Clasroom training batch schedules:

Location Day/Duration Date Time Type
Aundh Weekend 21/09/2019 09:00 AM Demo Batch Enquiry
Kharadi Weekend 21/09/2019 12:00 PM Demo Batch Enquiry
Kharadi Weekend 22/09/2019 12:00 PM New Batch Enquiry

Online training batch schedules:

Mode Day/Duration Start Date End Date ₹ Price Book Seat
Online 8 Weeks, 4 Days 16/02/2019 16/04/2019 ₹ 16000.00 Enroll Now

Hadoop Developer / Analyst / SPARK + SCALA / Hadoop (Java + Non- Java) Track

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

Best Bigdata Hadoop Training with 2 Real-time Projects with 1 TB Data set

Duration of the Training : 8 to 10 weekends 


Bigdata Hadoop Syllabus

For whom Hadoop is?

IT folks who want to change their profile in a most demanding technology which is in demand by almost all clients in all domains because of below mentioned reasons-

  •  Hadoop is open source (Cost saving / Cheaper)
  •  Hadoop solves Big Data problem which is very difficult or impossible to solve using highly paid tools in market
  •  It can process Distributed data and no need to store entire data in centralized storage as it is there with other tools.
  •  Now a days there is job cut in market in so many existing tools and technologies because clients are moving towards a cheaper and efficient solution in market named HADOOP
  •  There will be almost 4.4 million jobs in market on Hadoop by next year.

Please refer below mentioned links:


Can I Learn Hadoop If I Don’t know Java?


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.

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. 


Training Syllabus                                                   ,

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


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 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 Daemons – Resource Manager, NodeManager 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


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 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


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


Region Servers





How HBASE is differed from RDBMS

HDFS vs. HBase

Client-side buffering or bulk uploads

HBase Designing Tables

HBase Operations





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



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



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


Packages and Imports

Working with Lists, Collections

Abstract Members

Implicit Conversions and Parameters

For Expressions Revisited

The Scala Collections API


Modular Programming Using Objects



Architecture and Spark APIs

Spark components 

Spark master




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



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

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I completed my Hadoop in Radical.My Trainers teaching is probably the best training one could get for sure. You don't only get to learn but you also get the Experience level training here.
Author Rating

DataQubez University creates meaningful big data & Data Science certifications that are recognized in the industry as a confident measure of qualified, capable big data experts. How do we accomplish that mission? DataQubez certifications are exclusively hands on, performance-based exams that require you to complete a set of tasks. Demonstrate your expertise with the most sought-after technical skills. Big data success requires professionals who can prove their mastery with the tools and techniques of the Hadoop stack. However, experts predict a major shortage of advanced analytics skills over the next few years. At DataQubez, we’re drawing on our industry leadership and early corpus of real-world experience to address the big data & Data Science talent gap.

How To Become Certified Big Data – Hadoop Developer

Certification Code – DQCP – 502

Certification Description – DataQubez Certified Professional Big Data – Hadoop Developer

Exam Objectives
Configuration :-

Define and deploy a rack topology script, Change the configuration of a service using Apache Hadoop, Configure the Capacity Scheduler, Create a home directory for a user and configure permissions, Configure the include and exclude DataNode files

Troubleshooting :-

Restart an Cluster service, View an application’s log file, Configure and manage alerts Troubleshoot a failed job

High Availability :-

Configure NameNode, Configure ResourceManager, Copy data between two clusters, Create a snapshot of an HDFS directory, Recover a snapshot, Configure HiveServer2

Data Ingestion – with Sqoop & Flume :-

Import data from a table in a relational database into HDFS, Import the results of a query from a relational database into HDFS, Import a table from a relational database into a new or existing Hive table, Insert or update data from HDFS into a table in a relational database, Given a Flume configuration file, start a Flume agent, Given a configured sink and source, configure a Flume memory channel with a specified capacity

Data Transformation Using Pig :-

Write and execute a Pig script, Load data into a Pig relation without a schema, Load data into a Pig relation with a schema, Load data from a Hive table into a Pig relation, Use Pig to transform data into a specified format, Transform data to match a given Hive schema, Group the data of one or more Pig relations, Use Pig to remove records with null values from a relation, Store the data from a Pig relation into a folder in HDFS, Store the data from a Pig relation into a Hive table, Sort the output of a Pig relation, Remove the duplicate tuples of a Pig relation, Specify the number of reduce tasks for a Pig MapReduce job, Join two datasets using Pig, Perform a replicated join using Pig

Data Analysis Using Hive :-

Write and execute a Hive query, Define a Hive-managed table, Define a Hive external table, Define a partitioned Hive table, Define a bucketed Hive table, Define a Hive table from a select query, Define a Hive table that uses the ORCFile format, Create a new ORCFile table from the data in an existing non-ORCFile Hive table, Specify the storage format of a Hive table Specify the delimiter of a Hive table, Load data into a Hive table from a local directory Load data into a Hive table from an HDFS directory, Load data into a Hive table as the result of a query, Load a compressed data file into a Hive table, Update a row in a Hive table, Delete a row from a Hive table, Insert a new row into a Hive table, Join two Hive tables, Set a Hadoop or Hive configuration property from within a Hive query.

Data Processing through Spark & Spark SQL& Python :-

Frame big data analysis problems as Apache Spark scripts, Optimize Spark jobs through partitioning, caching, and other techniques, Develop distributed code using the Scala programming language, Build, deploy, and run Spark scripts on Hadoop clusters, Transform structured data using SparkSQL and DataFrames

For Exam Registration , Click here:

Trainer is having 17 year experience in IT with 10 years in data warehousing &ETL experience. It has been six years now that he has been working extensively in BigData ecosystem toolsets for few of the banking-retail-manufacturing clients. He is a certified HDP-Spark Developer and Cloudera certified Hbase specialist. He also have done corporate sessions and seminars both in India and abroadRecently he was engaged by Pune University for 40 hour sessions on BigData analytics to the senior professors of Pune.

All faculties at our organization are currently working on the technologies in reputed organization. The curriculum that is imparted is not just some theory or talk with some PPTs. We absolutely frame the forum in such a way so that at the end the lessons are imparted in easy language and the contents are well absorbed by the candidates. The sessions are backed by hands-on assignment. Also that the faculties are industry experience so during the course he does showcase his practical stories.

  • How we are Different from Others : Covers each topics with Real Time Examples . Covers 8 Real time project and more than 72+ Assignments which is divided into Basic , Intermediate and  Advanced . Trainer from Real Time Industry with 9 years experience in DWH. Working as BI and Hadoop consultant having 3+ years in Bigdata & Hadoop real time implementation and migrations.
    This is completely hands own training , which covers 90% Practical And 10% Theory .Here in Radical Technologies , we will take all prerequisite like Java ,SQL, which is required to learn Hadoop Developer and Analytical skills. This way We will accommodate technology illiterate and Technical experts in the same session and at the end of the training , they will gain the confidence  that , they got up-skilled to a different level. 
    • 8 Domain Based Project With Real Time Data ( with one trainer – two project. If you req more projects , you are free to attend any other trainers project orientations sessions )
    • 5 POC
    • 72 Assignments
    • 25 Real Time Scenarios On 16 Node Clusters ( Aws Cloud setup )
    • Basic Java
    • DWH Concept
    • Pig|Hive|Mapreduce|Nosql|Hbase|Zookeeper|Sqoop|Flume|Oozie|Yarn|Hue|Spark |Scala

    42 Hours Classroom Section

    30 Hours of assignments

    25 hours for One Project and 50 Hrs for 2 Project ( Candidates should prepare with mentor support . 50 hours mentioned is total hours spent on project by each trainer )

    350+ Interview Questions

    Administration and Manual Installation of Hadoop with other Domain based projects will be done on regular basis apart from our normal batch schedule .

    We do have projects from Healthcare , Financial , Automotive ,Insurance , Banking , Retail etc , which will be given to our students as per their requirements .

    • Training By 14+ Years experienced Real Time Trainer
    • A pool of 200+ real time Practical Sessions on Bigdata Hadoop
    • Scenarios and Assignments to make sure you compete with current Industry standards
    • World class training methods
    • Training  until the candidate get placed
    • Certification and Placement Support until you get certified and placed
    • All training in reasonable cost
    • 10000+ Satisfied candidates
    • 5000+ Placement Records
    • Corporate and Online Training in reasonable Cost
    • Complete End-to-End Project with Each Course
    • World Class Lab Facility which facilitates I3 /I5 /I7 Servers and Cisco UCS Servers
    •  Covers Topics other than from Books which is required for the IT Industry
    • Resume And Interview preparation with 100% Hands-on Practical sessions
    • Doubt clearing sessions any time after the course
    • Happy to help you any time after the course

ML and GraphX ,’R’ Language

Data Analytics / Science

Cloudera Certified Professional (CCP)

CCP Data Engineer

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