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Big Data And Hadoop Certification Training

The Big Data and Hadoop training course from Prwatech hadoop training in bangalore is designed to enhance your knowledge and skills to become a successful Hadoop developer. In-depth knowledge of core concepts will be covered in the course along with implementation on varied industry use-cases.

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Course Details 

Hadoop Training in Bangalore

Hadoop is another name for processing and storing large data sets in a proper computing environment, from where multiple data can be checked and used in one go. This open source, java based programming framework is a part of Apache project which is sponsored by Apache Software Foundation.

SKILLSET

Individuals who want to pursue this course should have a basic knowledge of Java and SQL. Also Hadoop is developed in such a way that it also helps students of non Java background. To help those categories out, Pig and Hive is developed for the sole purpose. However many course programs; online classes and classroom classes includes Java with Hadoop teaching module.

WHO ARE BENEFITTED?

Individuals from certain professions like mainframe developers, application developers and testers, fresher’s from the computer language background, architects, business developers; all can get immense benefit by learning this course. There are many options available in Pune owing to its latest increase in IT hubs.

ONLINE TRAINING

Online training benefits every category of professionals to learn this computer language. The online training classrooms are well equipped with experienced teachers, who conducts live classes to help the aspiring students. Also, the course material is distributed in PowerPoint or pdf forms covering each and every topic of the course. The students are well prepared there to come in terms with real life projects. Assistance is provided 24×7 and any confusion regarding the topic can be instantly solved.

COURSE

The course like data science and live hadoop training in Bangalore is designed keeping in mind the practical application and at the same time enabling the students to have detail knowledge on the subject. The syllabus is thus made with all the minute features in context to Hadoop. Below enlisted are the features of this course.

 

  • Hadoop fundamentals.
  • Hadoop Java API.
  • Fundamentals of working with Pig Latin.
  • Advanced knowledge on working with Pig Latin.
  • Hive Fundamentals.
  • Hive Advanced.
  • Hadoop on Amazon cloud.
  • HBase and Zookeeper.
  • Sqoop.
  • Flume.
  • Oozie and Hue.
  • Mongo DB.
  • Yarn Architecture.
  • Basics of Java for Hadoop.
  • Hadoop ecosystem and Big Data.

These are the basic inclusions in a Hadoop course and almost all the online portals provide the students with materials related to this course structure. However, there are multiple websites offering online courses and hence choosing the right online training center is the necessary thing.

CLASSES

The duration of classes vary in different institutes. Basically the classes are of 150 hours, where the entire syllabus is covered. The students are very well prepared for the certification and different monitoring exams and mock interviews are conducted for the future endeavors. Also, the students are given the chance on working with real life projects under the proper guidance of the team of faculties.

TUTORIALS

The online tutorials are available in various websites which can help in brushing up the concept more correctly. With the help of the tutorials, Hadoop can be tried at home. You just need to have the software downloaded and in working condition.

CERTIFICATION

At the end of the course from our team, the individual gets a Hadoop certification. This can be later used in different professions or even for getting admission to the nest version of the certification courses.

After completion of this module you will have a basic understanding of, what is Big data, limitation and the existing problems with Big Data. You will be able to comprehend how Hadoop is the solution to problems faced with Big Data. Along with that, you will know the Hadoop ecosystem components, architecture, HDF sand map reduce framework and anatomy of read and write file.

Sub topics that will be covered in this module are:

  1. Hadoop Cluster Architecture
  2. Hadoop Cluster configuration files
  3. Hadoop Cluster Modes
  4. Multi-Node Hadoop Cluster
  5. A Typical Production Hadoop Cluster
  6. Map Reduce Job execution
  7. Common Hadoop Shell commands
  8. Data Loading Techniques: Hadoop Copy Command
  9. Hadoop Project: Data Loading

Hadoop Cluster Configuration and Data Loading

Hadoop Cluster Architecture and Setup, important configuration files in a Hadoop Cluster and Data loading techniques would be the broad area of study in this module.

Sub topics that will be covered in this module are:

  1. Hadoop Cluster Architecture
  2. Hadoop Cluster configuration files
  3. Multi-Node Hadoop Cluster
  4. A Typical Production Hadoop Cluster
  5. Map Reduce Job execution
  6. Common Hadoop Shell commands
  7. Data Loading Techniques: Hadoop Copy Command
  8. Hadoop Project: Data Loading

This module would coverp you understand Multiple Hadoop Server such a Name node and Data node and also MapResduce data processing; you will also understand the Hadoop 1.0 cluster setup and configuration, steps in setting up Hadoop client using Hadoop 1.0, and important Hadoop configuration files and parameter.

Sub topics that will be covered in this module are:

  1. Hadoop Installation and Initial Configuration
  2. Developing Hadoop in fully-distributed mode
  3. Developing a multi-node Hadoop cluter
  4. Installing Hadoop Clients
  5. Hadoop server roles and their usage
  6. Rack Awareness
  7. Anatomy of write and read, replication pipeline Data Processing

In this module you will understand all the regular cluster administration tasks such as adding and removing data nodes, name node recovery, configuring Backup and recovery in Hadoop, diagnosing the node failure in the cluster, Hadoop upgrade etc.

Sub topics that will be covered in this module are:

  1. Setting up Hadoop backup
  2. Whitelist and blacklist data nodes in a cluster
  3. Setup quota’s upgrade Hadoop cluster
  4. Copy data across cluster using distcp
  5. Diagnostic and Recovery
  6. Cluster Maintenance, Configure Rack awareness

At the end of this module you will know what flume and why do we use them. We will also discuss the twitter data analysis using Hive

Sub topics that will be covered in this module are:

  1. What is Flume?
  2. Why Flume?
  3. Important Data using Flume
  4. Twitter Data Analysis using Hive

In this module you will learn about analytics with PIG, Pig Latin scripting, complex data type, different cases to work with PIG. You will also study execution environment, operation & transformation.

Sub topics that will be covered in this module are:

  1. Execution Types
  2. Grunt Shell
  3. Pig Latin
  4. Data Processing
  5. Schema on read Primitive data types and complex data types
  6. Tuple Schema, BAG Schema & Map Schema
  7. Loading and Storing
  8. Filtering, Grouping & Joining Debugging commands
  9. Validations in PIG
  10. Type casting in PIG
  11. Working with Functions

This module would cover important concept regarding Sqoop and the import and export of data from RDBMS (MySql, Oracle) to HDFS & Vice Versa.

Sub topics that will be covered in this module are:

  1. Execution Types
  2. Grunt Shell
  3. Pig Latin
  4. Data Processing
  5. Schema on read Primitive data types and complex data types
  6. Tuple Schema, BAG Schema & Map Schema
  7. Loading and Storing
  8. Filtering, Grouping & Joining Debugging commands
  9. Validations in PIG
  10. Type casting in PIG
  11. Working with Functions

This module will cover all the advance HB concepts. You will also learn what zookeeper is and how it helps in monitoring a cluster. You will also study why HBase uses Zookeeper and how to build application with Zookeeper.

Sub topics that will be covered in this module are:

  1. Execution Types
  2. Grunt Shell
  3. Pig Latin
  4. Data Processing
  5. Schema on read Primitive data types and complex data types
  6. Tuple Schema, BAG Schema & Map Schema
  7. Loading and Storing
  8. Filtering, Grouping & Joining Debugging commands
  9. Validations in PIG
  10. Type casting in PIG
  11. Working with Functions

In this module, we will discuss the newly added features in Hadoop 2.0, called Yarn, MRv2, Name Node High Availability, HDFS Federation, supports for windows etc.

Sub topics that will be covered in this module are:

  1. Hadoop 2.0 New Feature: Name Node High Availability
  2. HDFS Federation
  3. MRv2
  4. YARN
  5. Running MRv1 in YARN
  6. Upgrade your existing MRv1 code to MRv2

In this module we will discuss Map Reduce Framework in detail. How Map Reduce implement on Data which is stored in HDFS. Know about input split, input format & output format. The overall Map Reduce Process & different stages to process in data and many more.

Sub topics that will be covered in this module are:

  1. Map Reduce Concepts
  2. Mapper Reducer
  3. Driver
  4. Input Split (Input Formats (Input Splits and records, Text Input, Binary Input, Multiple Inputs)
  5. Record Reader
  6. Overview of Input File Formats
  7. Hadoop Project: Map Reduce Programming

This is a very interesting and important topic that is covered in this course. After completion of this module you will be able to answer about Hive and Hive Web Interface. In this module you would be given hands on exercises with the real life problem.

Sub topics that will be covered in this module are:

  1. Hive Services, Hive Shell, Hive Server and Hive Web Interface (HWI
  2. Meta store
  3. Working and Partitions
  4. External partitioned tables
  5. Map the data to the partition in the table
  6. Difference between ORDER BY, DISTRIBUTE BY and SORT BY
  7. Bucketing and Sorted Bucketing with Dynamic partition
  8. RC File, ORC, SERDe: Regex
  9. INDEXES and VIEWS
  10. Log Analysis on Hive
  11. Access HBASE tables using Hive
  12. Hands on Exercises

Finally, in this last module you will understand what are Oozie and its architecture? This will also cover the configuration and the kinds of nodes. In this module we will also be discuss the kind of jobs you will get in Oozie.

Sub topics that will be covered in this module are:

  1. What is Oozie
  2. Architecture
  3. Developing & Running an Oozie Workflow (Mapreduce, Hive, Pig, Sqoop)
  4. Configure Oozie Workflows
  5. Kinds of Nodes
  6. Kinds of Oozie Jobs

After completion of this module and data science courses in bangalore you will have a basic understanding of, what is Big data, limitation and the existing problems with Big Data. You will be able to comprehend how Hadoop is the solution to problems faced with Big Data. Along with that, you will know the Hadoop ecosystem components, architecture, HDF sand map reduce framework and anatomy of read and write file.

Sub topics that will be covered in this module are:

  1. Hadoop Cluster Architecture
  2. Hadoop Cluster configuration files
  3. Hadoop Cluster Modes
  4. Multi-Node Hadoop Cluster
  5. A Typical Production Hadoop Cluster
  6. Map Reduce Job execution
  7. Common Hadoop Shell commands
  8. Data Loading Techniques: Hadoop Copy Command
  9. Hadoop Project: Data Loading

Hadoop Cluster Configuration and Data Loading

Hadoop Cluster Architecture and Setup, important configuration files in a Hadoop Cluster and Data loading techniques would be the broad area of study in this module.

Sub topics that will be covered in this module are:

  1. Hadoop Cluster Architecture
  2. Hadoop Cluster configuration files
  3. Multi-Node Hadoop Cluster
  4. A Typical Production Hadoop Cluster
  5. Map Reduce Job execution
  6. Common Hadoop Shell commands
  7. Data Loading Techniques: Hadoop Copy Command
  8. Hadoop Project: Data Loading

This module would coverp you understand Multiple Hadoop Server under courses like live Hadoop training in Bangalore such a Name node and Data node and also MapResduce data processing; you will also understand the Hadoop 1.0 cluster setup and configuration, steps in setting up Hadoop client using Hadoop 1.0, and important Hadoop configuration files and parameter.

Sub topics that will be covered in this module are:

  1. Hadoop Installation and Initial Configuration
  2. Developing Hadoop in fully-distributed mode
  3. Developing a multi-node Hadoop cluter
  4. Installing Hadoop Clients
  5. Hadoop server roles and their usage
  6. Rack Awareness
  7. Anatomy of write and read, replication pipeline Data Processing

In this module you will understand all the regular cluster administration tasks such as adding and removing data nodes, name node recovery, configuring Backup and recovery in Hadoop, diagnosing the node failure in the cluster, Hadoop upgrade etc.

Sub topics that will be covered in this module are:

  1. Setting up Hadoop backup
  2. Whitelist and blacklist data nodes in a cluster
  3. Setup quota’s upgrade Hadoop cluster
  4. Copy data across cluster using distcp
  5. Diagnostic and Recovery
  6. Cluster Maintenance, Configure Rack awareness

At the end of this module you will know what flume and why do we use them. We will also discuss the twitter data analysis using Hive

Sub topics that will be covered in this module are:

  1. What is Flume?
  2. Why Flume?
  3. Important Data using Flume
  4. Twitter Data Analysis using Hive

In this module you will learn about analytics with PIG, Pig Latin scripting, complex data type, different cases to work with PIG. You will also study execution environment, operation & transformation.

Sub topics that will be covered in this module are:

  1. Execution Types
  2. Grunt Shell
  3. Pig Latin
  4. Data Processing
  5. Schema on read Primitive data types and complex data types
  6. Tuple Schema, BAG Schema & Map Schema
  7. Loading and Storing
  8. Filtering, Grouping & Joining Debugging commands
  9. Validations in PIG
  10. Type casting in PIG
  11. Working with Functions

This module would cover important concept regarding Sqoop and the import and export of data from RDBMS (MySql, Oracle) to HDFS & Vice Versa.

Sub topics that will be covered in this module are:

  1. Execution Types
  2. Grunt Shell
  3. Pig Latin
  4. Data Processing
  5. Schema on read Primitive data types and complex data types
  6. Tuple Schema, BAG Schema & Map Schema
  7. Loading and Storing
  8. Filtering, Grouping & Joining Debugging commands
  9. Validations in PIG
  10. Type casting in PIG
  11. Working with Functions

This module will cover all the advance HB concepts. You will also learn what zookeeper is and how it helps in monitoring a cluster. You will also study why HBase uses Zookeeper and how to build application with Zookeeper.

Sub topics that will be covered in this module are:

  1. Execution Types
  2. Grunt Shell
  3. Pig Latin
  4. Data Processing
  5. Schema on read Primitive data types and complex data types
  6. Tuple Schema, BAG Schema & Map Schema
  7. Loading and Storing
  8. Filtering, Grouping & Joining Debugging commands
  9. Validations in PIG
  10. Type casting in PIG
  11. Working with Functions

In this module, we will discuss the newly added features in Hadoop 2.0, called Yarn, MRv2, Name Node High Availability, HDFS Federation, supports for windows etc.

Sub topics that will be covered in this module are:

  1. Hadoop 2.0 New Feature: Name Node High Availability
  2. HDFS Federation
  3. MRv2
  4. YARN
  5. Running MRv1 in YARN
  6. Upgrade your existing MRv1 code to MRv2

In this module we will discuss Map Reduce Framework in detail. How Map Reduce implement on Data which is stored in HDFS. Know about input split, input format & output format. The overall Map Reduce Process & different stages to process in data and many more.

Sub topics that will be covered in this module are:

  1. Map Reduce Concepts
  2. Mapper Reducer
  3. Driver
  4. Input Split (Input Formats (Input Splits and records, Text Input, Binary Input, Multiple Inputs)
  5. Record Reader
  6. Overview of Input File Formats
  7. Hadoop Project: Map Reduce Programming

This is a very interesting and important topic that is covered in this course. After completion of this module you will be able to answer about Hive and Hive Web Interface. In this module you would be given hands on exercises with the real life problem.

Sub topics that will be covered in this module are:

  1. Hive Services, Hive Shell, Hive Server and Hive Web Interface (HWI
  2. Meta store
  3. Working and Partitions
  4. External partitioned tables
  5. Map the data to the partition in the table
  6. Difference between ORDER BY, DISTRIBUTE BY and SORT BY
  7. Bucketing and Sorted Bucketing with Dynamic partition
  8. RC File, ORC, SERDe: Regex
  9. INDEXES and VIEWS
  10. Log Analysis on Hive
  11. Access HBASE tables using Hive
  12. Hands on Exercises

Finally, in this last module you will understand what are Oozie and its architecture? This will also cover the configuration and the kinds of nodes. In this module we will also be discuss the kind of jobs you will get in Oozie.

Sub topics that will be covered in this module are:

  1. What is Oozie
  2. Architecture
  3. Developing & Running an Oozie Workflow (Mapreduce, Hive, Pig, Sqoop)
  4. Configure Oozie Workflows
  5. Kinds of Nodes
  6. Kinds of Oozie Jobs

Prwatech has trained and placed more than x+ students. We have placed the unbiased
reviews and complaints written by our students who has trained in our training network in
Bangalore and Pune for the benefits of our visitors and potential training seekers in Prwatech centre
has been rated for high quality for training, experienced trainers and excellent placement assistance.

Reviewed byx on date the training facilities available in Prwatech training are really
good. I am happy that i have choosen Prwatech training for my x course training in Bangalore.

Reviewed byX on date I had excellent trainer for Hadoop and superb lab facilities, we
always recommend Prwatech institute for Hadoop training in Pune.

Prwatech is one of the best cost-effective solutions which deals in live hadoop training and data science courses in Bangalore to learn online and offline. I enrolled in their Big Data Hadoop and Spark Developer course. The course content was really nice. The certification helped me to land a job in X Pvt Ltd. as Hadoop Developer.

I have enrolled for Big-Data Hadoop and Spark Developer from Prwatech. The course was
explained using very simple and real-time analogies which helped me to understand the concepts
and also do exercises easily. The trainer was really helpful and was always willing to share the
knowledge with the wider audience. I highly recommend to Prwatech.

Prwatech is very good institute I join for Hadoop certification .Trainer explain all concept
in very easy manner.

I have done my Machine Learning training from Prwatech. It is one of the best institutes in
Bangalore and Pune. The best thing about the institute is their experienced staff and upgraded labs.

I am X-name from Bangalore. By my friend reference I taken admission in Prwatech
training institute in Bangalore. Really very good institute now I am working in a MNC in Bangalore.

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35 Hours
Practical 40 Hours
15 Seats
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