Big Data Hadoop Training

Big Data Hadoop Course

EduPristine Big Data Hadoop training program is specially designed to master the latest and core components of Hadoop like Map-reduce, HBASE, PIG, HIVE, ZOOKEEPER, SQOOP, Oozie with Hue and plus complementary session on Java Essentials for Hadoop, Python and Unix sessions.

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DID YOU
KNOW?

90% of the data in the world today has been created in the last two years alone.

Source:
IBM Big Data

About COURSE

Big data Hadoop online training program not only prepare candidate with the vital concepts of Hadoop, but also provide the required work experience in Big Data and Hadoop via implementation of real time industry projects.Read more

Big data Hadoop Live Online Classes are being conducted using professional grade IT conferencing system from Citrix. The students can interact with the faculty in real-time during the class using chat and voice. The students will be required to install a light-weight IT application on their device which could be a laptop, desktop, tablet or a mobile. Citrix supports Windows, iOS operating system and recommends an internet speed of 1 MBPS at the user's end.

  • Extensive Online Training

    16 day online Training.

  • Live Project

    We will provide 2 data sets to work on real life projects.

  • Hadoop Trends

    3Hrs online interactive session on every 2nd & 4th Sunday of month for all alumni students
    - New topics in Big data & Industrial Case studies will be covered.
    - Access to Hadoop trends for a year.
    - 4 classroom workshops in a year in 5 cities to cover important topics.
    - Cities for workshop- Mumbai, Pune, Bangalore, Kolkata and Delhi only

  • Online Materials

    Topic Wise study material in the form of Presentation and Case Studies
    -PowerPoint Presentation covering all classes -Code files for each case study
    -Recorded Videos of Live Instructor based Training
    -Recorded Videos Covering all classes
    -Quiz/Assignment with detailed answers and explanation
    -Job Oriented Questions to prepare for Certification Exams
    -Doubt solving forum to interact with faculty & fellow students

  • Complimentary Course

    "Java Essentials for Hadoop" and UNIX session

  • 24x7 Online Access

    Access to Course Material (Presentations) etc.

Course Structure

Day

Topic

Day 1Introduction to Unix
Day 2 & 3Introduction to Java
Day 4Introduction to HDFS & Pseudo cluster environment
Day 5 & 6Understanding Map-Reduce Basics, Types & Formats
Day 7HIVE
Day 8Impala
Day 9PIG
Day 10Zookeeper
Day 11SQOOP
Day 12Live Project - 1
Day 13HBASE
Day 14Live Project - 2
Day 15Spark - I
Day 16Spark - II

About COURSE

Big Data and Hadoop classroom training designed by world’s leading data experts and prepare you for the Cloudera (CCA-175) preparation. Hadoop is a software framework for storing and processing Big Data. It is an open-source tool build on java platform and focuses on improved performance in terms of data processing on clusters of commodity hardware.Read more

  • Hadoop comprises of multiple concepts and modules like HDFS, Map-Reduce, HBASE, PIG, HIVE, SQOOP and ZOOKEEPER to perform the easy and fast processing of huge data.

  • Hadoop conceptually different from Relational databases and can process the high volume, high velocity and high variety of data to generate value.

  • Big Data Hadoop Pro Training

    15 days Classroom (75 hours) + 4 days Online Training (12 Hours) (Java, Unix & Python)

  • Live Project

    We will provide 2 data sets to work on real live projects.

  • Online Materials

    Topic Wise study material in the form of Presentation and Case Studies - PowerPoint Presentation covering all classes - Code files for each case study
    - Recorded Videos of Live Instructor based Training
    - Recorded Videos Covering all classes
    - Quiz/Assignment with detailed answers and explanation
    - Job Oriented Questions to prepare for Certification Exams
    - Doubt solving forum to interact with faculty & fellow students

  • Complimentary Course

    "Java Essentials for Hadoop", Python and UNIX session

  • 24x7 Online Access

    24x7 Online Access to Course Materials.

Course Structure

  • Overview of Big Data Technologies and its role in Analytics
  • Big Data challenges & solutions
  • Data Science vs Data Engineering
  • Job Roles, Skills & Tools

Setting up Development Environment

  • Setting up Development environment on User's laptop to be able to develop and execute programs
  • Setting up Eclipse ( Basics of Eclipse like, import, create project, add JARs) to understand basics of Eclipse for Map Reduce and Spark development
  • Installing Maven & Gradle to understand building tools
  • Installing Putty, FileZilla/WinSCP to get ready to access EduPristine's Big Data Cloud

Case Study: XYZ Telecom need to set up approprioate directory Structure along with permissions on various files on Linux file system

  • Setting up, accessing and verifying Linux server access over SSH
  • Transferring files over FTP or SFTP
  • Creating directory structure and Setting up permissions
  • Understanding File name pattern and move using regular expressions
  • Changing file owners, permissions
  • Reviewing mock file generator utility written in Shell Script, enhancing it to be more useful

Case Study: Developing a simulator to generate mock data using Python 

  • Understand your domain requirement, describe need of required fields and possible values, file format etc
  • Preparing configuration file that can be changed to fit any requirement
  • Developing python script to generate mock data in configuration file

Case Study: Design and Develop Phone Book in Java

  • Identifying Classes and Methods for Phone Book
  • Implementing design into Java Code using Eclipse
  • Compiling and Executing Java Program
  • Enhancing the code with each learnings, like Inheritence, Method overloading
  • Further enhancing the code to initialize PhoneBook from a Text File by using Java file reading

Case Study: Handling huge data set in HDFS to make it accessible to right user and remove non-functional requirements like backups, cost, high availability etc.

  • Understanding the problem statement and challenges persisting to such large data to perceive the need of Distributed File System
  • Understanding HDFS architecture to solve problems
  • Understanding configuration and creating directory structure to get a solution of the given problem statement
  • setup appropriate permissions to secure data for appropriate users

Case Study: Developing automation tool for HDFS file management

  • Setting up Java Development with HDFS libraries to use HDFS Java APIs
  • Coding to develope menu driven HDFS file management utility and schedule to run for file management in HDFS cluster

Sqoop

Case Study: Develop automation utility to migrate huge RDBMS warehouse implemented in MySQL to Hadoop cluster

  • Creating and loading data into RDBMS table to understand RDBMS setup
  • Preparing data to experiment with Sqoop imports
  • Importing using Sqoop Command in HDFS file system to understand simple imports
  • Importing using Sqoop command in Hive table to import data into Hive partitioned table and perform ETL
  • Exporting using Sqoop from Hive/ HDFS to RDBM to store the output of Hive ETL into RDBMS
  • Wrapping Sqoop commands into Unix Shell Script To be able to build and use automated utility for day to day use

Map-Reduce

Case Study: Processing 4G usage data of a Telecom Operator to find out potential customers for various promotional offers

  • Cleaning data, ETL and Aggregation
  • Exploring data set using known tools like linux commands to understand the nature of data
  • Setting up Eclipse project, maven dependencies to add required Map Reduce Libraries
  • Coding, packaging and deploying project on hadoop cluster to understand how to deploy/ run map reduce on Hadoop Cluster

Case Study: Process a structured data set to find some insights

  • Finding out per driver total miles and hours driven
  • Creating Table, Loading Data, Selecting Query to load, query and cleaning of data
  • Which driver has driven maximum & minimum miles
  • Joining Tables, Saving Query results to table to explore and use right type of table type, partition schema, buckets
  • Discussing optimum file format for hive table
  • Using right file format, type of table, partition scheme to optimize query performance
  • Using UDFs to reuse domain specific implementations

Case Study: Perform ETL processing on Data Set to find some insights

  • Loading and exploring Movie - 100K data set to load data set, explore it and associate schema to it
  • Using grunt, Loading data set, defining schema
  • Finding Simple Statistic from given Data Set to clean up the data
  • Filtering and modifying data schema
  • Finding gender distribution in users
  • Aggregrating and looping
  • Finding top 25 movies by rating, joining data sets and saving to HDFS to perform aggregration
  • Dumping, Storing, joining, sorting
  • Filtering function for complex condition to reuse domain specific functionalities & avoid rewriting code
  • Using UDFs

Case Study: Build a model to predict production error/ failure (huge servers - applications/ software) with good speed by using computation power efficiently while considering processor challenges

  • Loading and performing pre-processing to convert unstructured data to some structured data format
  • Cleaning data, filtering out bad records, converting data to more usable format
  • Aggregating data based on Response Code to find out server' performace from logs
  • Filtering, Joining and aggregating data to find top 20 Frequent Hosts that generates errors

Spark Project

Case Study: Build a model (using Python) to predict production error/ failure (huge servers - applications/ software) with good speed by using computation power efficiently while considering processor challenges

  • Loading and performing pre-processing to convert unstructured data to some structured data format
  • Cleaning data, filtering out bad records, converting data to more usable format
  • Aggregating data based on Response Code to find out server' performace from logs
  • Filtering, Joining and aggregating data to find top 20 Frequent Hosts that generates errors

Case Study: Setting up Data processing pipeline to work as per schedule in Hadoop Eco System comprising of multiple components like sqoop job, hive scripts, pig scripts, spark jobs etc.

  • Setting up Oozie workflow to tigger a script, then Sqoop Job followed by Hive Job
  • Executing workflow to run complete ETL pipeline

Case Study: Find out top 10 customers by expenditure, top 10 most buying brands, and monthly sales from data stored in Hbase which is in Key value pair

  • Designing Hbase Table Schema to model table structure, decide families in table as per data
  • Deciding families in table as per data
  • Bulk Loading & Programatically Loading data using Java APIs to populate data into Hbase table
  • Querying and Showing data on UI to integret Hbase with UI/Reporting

Project: ETL processing of retail logs

  • To find demand of a given product
  • Trend and seasonality of a product
  • Understand performance of the chain

Project: Creating 360 degree view (past, present and future) of the customer for a retail company - avoiding repetition or re-keying of information, to view customer history, establishing context and initiating desired actions

  • Explorating and checking basic quality of data to understand data and need for filtering/pre-processing of data
  • Loading data into RDBMS table to simulate real world scenarios where data is persisted in RDBMS table
  • Developing & executing Sqoop Job to ingest Data in Hadoop Cluster to perfrom further actions
  • Developing & executing Pig script to perform required ETL processing on ingested Data
  • Developing & executing Hive Queries to get reports out of processed data

Project: Twitter Sentiment Analytics - Collect and real time data (JSON format), and perform sentiment analysis on continuously flowing streaming data

  • Creating and setting up Twitter App to generate Twitter Auth tokens to access twitter by APIs
  • Building Flume Source to pull twits from Twitter
  • Setting up Flume agent with Kafka sink to persist twits into Distributed Kafka topic
  • Setting up Flume agent with Kafka Source and HDFS as Sink to backup twits on HDFS for batch processing
  • Building & Executing Spark Job to perform Sentiment Analytics in real time on each incoming twits
  • Creating Hive table on Twits and perform basic queries to understand Hive Serdes and dealing with Semi-structured data in Hive
  • Writing and Executing Impala Queries to understand and overcome Impala's limitation of not being able to use Hive Serdes

Project: Machine Learning with TensorFlow - Build a solution which can recognize images on search words and can run on distributed computing like Hadoop/ Spark etc. for a photo storage company

  • Setting up development environment to be able to use TensorFlow java APIs
  • Developing Java Code using TensorFlow Inception Model using Java API to develop image recognition program in Java
  • Developing Python code to train TensorFlow model to learn to training TensorFlow model, for a domain specific problem
  • Project: Developing a Chat-bot to offer an artificially intelligent customer help desk for an insurance company

    • Identify client's most frequently asked questions and answers
    • Develop a training data set
    • Build TensorFlow NLP model which can understand questions
    • Train the model
    • Implement & Run the model

Real Time Analytics, Unstructured Data Ingestion

An open source database that uses a document-oriented data model

Exam pattern, CV preparation & Imp topics

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Benefits WHY?

  • In our PLUS program, you would be entitled to attend live classes on new topics such as updated software, latest programs etc for an entire year.
  • Topics like PYTHON, machine learning, Kafka, Scala, Cassandra, R integration with Hadoop etc. This way you can stay updated with all the latest trends coming in the big data market.
  • You would notice that we are one of the few providers that provide SPARK as part of our package. So basically, you would be learning all of these great programs at the same place and that too at a affordable price.
  • Our sessions are conducted by industry expert faculty members, where you would have a complete hands-on experience during the program.

The average salary for big data analytic professionals in the non-managerial role is

INR 8.5 lakhs

per annum.

Reviews WHAT OTHERS SAY

The Big Data course from EduPristine was quite awesome, as this Big Data Hadoop Training in Pune was very beneficial to groom my carrier ahead. This course will surely upgrade my carrier, in future. In EduPristine i found Faculty which was highly qualified, experienced and the study material which was provided by institute was really helpful. Support team was really at there work for us. Overall i found the best institute. Thanks EduPristine.

Honnesh Kumar BE in Information Science

I had completed my Big Data Hadoop course from EduPristine. Following are the Pros. and Cons. of it. Pros.1. Good Study material but there is a scope of improvement. 2. Flexibility of Batch selection 3. Classroom as well as Webinar classes. 4. You can attend the any class even after completion of your Course.5. The only institute which provide Hadoop training.

Sagar Kaplay Senior Engineer - Application Development at SunGard

Who should do this? TARGET

This Big Data Hadoop course is designed for professionals aspiring to make a career in Big Data Analytics using Hadoop Framework. Software Professionals, Analytics Professionals, ETL developers, Project Managers, Testing Professionals and IT freshers are the key beneficiaries of this course.
The prerequisites for learning Hadoop include hands-on experience in Core Java and good analytical skills to grasp and apply the concepts in Hadoop. We provide a complimentary Course "Java Essentials for Hadoop" to all the participants who enroll for the Hadoop Training.

FAQsWHY?

Pre-requisites for learning Hadoop ?

Is it a classroom program? Who are the instructors?

When are the classes held? What if I miss a class?

What if I have queries after I complete this course?

Can I join the next batch, so that Java is understood well?

How will I get the recorded sessions?

How long is the Hadoop course? Will I be working on a Project?

Do you provide any Certification? If yes, what is the Certification process?

What if I am not able to clear the Certification exam in 1st attempt?

Do I need to pay for the Re-Attempt of the Certification exam?

Can I Install Hadoop on my Mac Machine? What are the system requirements?

I have around experience in software development. What are the career prospects?

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