Big Data Hadoop Development / Testing Certification Training

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4627 Reviews 4.9

Learners : 1080

Duration :  30 Days

About Course

Basic and advanced Hadoop concepts are covered in the Big Data Hadoop Online Training. Both professionals and beginners can benefit from our Hadoop training. The open-source framework for big data is called Hadoop. Apache offers it for processing and analyzing very large volumes of data. It is presently used by Google, Facebook, LinkedIn, Yahoo, Twitter, etc., and was created in Java. All Big Data Hadoop subjects are covered in our Hadoop training, including HDFS, MapReduce, Yarn, Hive, HBase, Pig, Sqoop, and others. India’s top online training institution.

Big Data Hadoop Training Course Syllabus


✔ What is Big Data?
✔ Examples of Big Data
✔ Reasons for Big Data Generation
✔ Why Big Data deserves your attention
✔ Use cases of Big Data
✔ Different options of analyzing Big Data
✔ Interview Questions and Tips.
✔ Cloudera 5.12.0 Hadoop latest software


✔ What is Big data Hadoop
✔ History of Hadoop
✔ How Hadoop’s name was given
✔ Advantages and drawbacks of Hadoop
✔ Problems with Traditional Large-Scale Systems and the Need for Hadoop
✔ Setting up single node Hadoop cluster(Pseudo mode)
✔ Understanding Hadoop configuration files
✔ Hadoop Components- HDFS, Map Reduce
✔ Overview Of Hadoop Processes
✔ Overview Of Hadoop Distributed File System
✔ Understanding Hadoop Architecture 1X
✔ Understanding Hadoop Architecture 2X
✔ Fundamental of HDFS (Blocks, Name Node, Data Node, Secondary Name Node)
✔ Rack Awareness
✔ Read/Write from HDFS
✔ HDFS Federation and High Availability
✔ The building blocks of Hadoop
✔ Types of scaling
✔ Hands-On Exercise using Linux commands
✔ Hands-On Exercise using HDFS commands
✔ HDFS Module Interview Questions and Answers

MODULE 3 – HIVE in Big data Hadoop

✔ Introduction to Apache Hive
✔ Architecture of Hive
✔ Installing Hive
✔ Hive data types
✔ Exploring hive meta store tables
✔ Types of Tables in Hive(Internal Table and External Table)
✔ Comparison between internal table and external table
✔ Loading data into hive tables
✔ Writing output into local or hdfs file system
✔ How to use hive commands in Linux prompt
✔ How to use Hadoop commands in hive prompt
✔ Partitions(Static & Dynamic)
✔ Bucketing
✔ Table Sampling
✔ Hive functions(Numeric Functions, Aggregate Functions, Date Functions, and String Functions)
✔ Indexes
✔ Views
✔ Sub Quires
✔ Joins (Left Outer, Right Outer, Full Outer, inner join, and Cross Join)
✔ Developing hive scripts
✔ Parameter Substitution
✔ Difference between order by & sort by
✔ Difference between Cluster by & distributed by
✔ Difference between view and table
✔ Purpose of UNION ALL
✔ DML Commands
✔ Complex Data Types(Array, Map and Struct)
✔ File Input formats
✔ Text File
✔ RC
✔ Sequence
✔ Avro
✔ Parquet
✔ Creating Hive UDFs
✔ Hands-On Exercise
✔ Hive module interview questions and answers


✔ Introduction to Apache Pig
✔ Building Blocks ( Bag, Tuple & Field)
✔ Installing Pig
✔ PIG Architecture
✔ Big Data types and complex data types
✔ Pig components
✔ Different modes of execution of PIG
✔ Types of different bags creation
✔ Working with various PIG Commands covering all the functions in PIG
✔ Aggregate and String functions.
✔ Developing PIG scripts
✔ Parameter Substitution
✔ Command line arguments
✔ Passing parameters through a param file
✔ Joins (Left Outer, Right Outer, Full Outer, inner join, replicated and skewed join)
✔ Advanced Joins (replicated join and skewed join)
✔ Purpose of count and count_star
✔ Group and cogroup
✔ Nested queries
✔ Word Count program using pig queries
✔ Working with Semi-structured data like XML and JSON
✔ Complex data types using pig
✔ Creating PIG UDFs
✔ Hands-On Exercise
✔ PIG Module Interview Questions and Answers


✔ Introduction to SQOOP
✔ SQOOP Architecture
✔ Import data from RDBMS to HDFS
✔ Importing Data from RDBMS to HIVE
✔ Importing data from RDBMS to HBASE
✔ Exporting data from HDFS to RDBMS
✔ Exporting data from HIVE to RDBMS
✔ Handling incremental loads using sqoop to HDFS.
✔ Handling incremental loads using sqoop to HIVE.
✔ Handling incremental loads using sqoop to Hbase.
✔ Creation of Sqoop jobs, Execution, and deletion.
✔ Hands-on exercise
✔ SQOOP Module Interview Questions and answers


✔ Introduction to HBase
✔ HBase Architecture
✔ Installation of HBase
✔ Exploring HBASE Master & Region server
✔ Exploring Zookeeper
✔ CRUD Operation of Hbase with Examples
✔ HIVE integration with HBASE(HBASE-Managed hive tables)
✔ Hands-on exercise on HBASE
✔ Hbase Interview Questions and Answers

MODULE 7 – MAP REDUCE in Big data Hadoop

✔ Understanding Map Reduce
✔ Job Tracker and Task Tracker
✔ Architecture of Map Reduce
✔ Map Function
✔ Reduce Function
✔ Data Flow of Map Reduce
✔ Hadoop Writable, Comparable & comparison with Java data types
✔ Map Function & Reduce Function
✔ How Map Reduce Works
✔ Submission & Initialization of Map Reduce Job
✔ Monitoring & Progress of Map Reduce Job
✔ Understand Difference Between Block and Input Split
✔ Role of Record Reader, Shuffler, and Sorter
✔ File Input Formats(TextInputFormat, KeyValueInputFormat)
✔ File output
✔ Formats(TextInputFormat,KeyValueInputFormat,SequenceOutputFormat)
✔ Getting Started With Eclipse IDE
✔ Setting up Eclipse Development Environment
✔ Creating Map Reduce Projects
✔ Configuring Hadoop API on Eclipse IDE
✔ Map Reduce program flow with word count
✔ Combiner & Partitioner, Custom Partitioner
✔ Joining Multiple datasets in Map Reduce
✔ Map Reduce programs explanation

MODULE 8 – OOZIE in Big Data Hadoop

✔ Oozie Introduction
✔ Oozie Architecture
✔ Oozie Map Reduce Jobs Execution
✔ How to use the OOZIE Web-Console
✔ How to use oozie through Hue

MODULE 9 – HUE in Big data Hadoop

✔ Use of Hue
✔ How to use HDFS module through hue
✔ How to use Hive module through hue
✔ How to use OOZIE module through hue
✔ How to use Hbase module through hue


✔ Purpose of Cloudera manager
✔ How to use Cloudera manager
✔ How to start cluster and stop cluster
✔ How to restart and stop only specific modules
✔ How to check existing cluster nodes
✔ How to remove node from the cluster
✔ How to monitor health issues of the cluster


✔ All the modules-related materials soft copies will provide
✔ Each module-related complete reference book will provide
✔ Big Data Hadoop Certification Papers will provide

Live Instructor Based Training With Software
Lifetime access and 24×7 support
Certification Oriented content
Hands-On complete Real-time training
Get a certificate on course completion
Flexible Schedules
Live Recorded Videos Access
Study Material Provided

Big Data Hadoop Training - Upcoming Batches

7th NOV 2022



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  • Practical Use Cases
  • Design Your Own Syllabus Based
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Big Data Hadoop Training FAQ'S

What is Big Data Hadoop?

Big Data Hadoop is a distributed computing platform. It is written in Java. It consists of the features like Google File System and MapReduce.

What platform and Java version are required to run Hadoop?

Java 1.6.x or higher versions are good for Hadoop, preferably from Sun. Linux and Windows are the supported operating system for Hadoop, but BSD, Mac OS/X, and Solaris are more famous for working.

What kind of Hardware is best for Big Data Hadoop?

Big Data Hadoop can run on a dual processor/ dual core machines with 4-8 GB RAM using ECC memory. It depends on the workflow needs.

What are the most common input formats defined in Hadoop?

These are the most common input formats defined in Hadoop:

  • TextInputFormat
  • KeyValueInputFormat
  • SequenceFileInputFormat

TextInputFormat is a by default input format.

Explain the use of .mecia class?

For the floating of media objects from one side to another, we use this class.

Get ahead in your career by learning Bigdata Hadoop through VISWA Online Trainings


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Tarunasree Gowra
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Tejaswini Kommu
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Chaitu Viswa
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