HADOOP DEV + SPARK & SCALA TRAINING IN BANGALORE

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Overview

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.

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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 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

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

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

 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.

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

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

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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