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Hadoop Developer Course

Hadoop Developer training course from Techybees provides participants expertise in all the steps to help you become a top Hadoop Developer. During this course, our expert instructors will help you.(some thing like this, because we also provide online training on other courses)

New Batch Details

Free Demo04 May 2017. 8:30 PM EST
Start Date12th May 2017
DaysMon, Wed & Fri
Time08:30 PM EST
TypeOnline, Live Instructor Led

This course helps you become a strong Hadoop developer by acquiring skills in Hadoop Distributed File System, Hadoop Cluster, Map-Reduce, Pig, Hive, Sqoop, and Hbase Zookeeper etc.

Towards the end of the course, you will get an opportunity practice these newly learned skills by doing real-world projects. Through these, you will work on a live project where you will be using PIG, HIVE, HBase and MapReduce to perform Big Data analytics.

This course is taught by a Senior Hadoop Developer, who is an early entrant into this technology and continues to work on various Big Data projects. This course is augmented by industry professionals who use the technology and share practical use cases.

What you'll learn

During this course, our instructors will help you:
  1. 1. Master the concepts of HDFS and MapReduce framework
  2. 2. Understand Hadoop 2.x Architecture
  3. 3. Setup Hadoop Cluster and write Complex MapReduce programs
  4. 4. Learn data loading techniques using Sqoop and Flume
  5. 5. Perform data analytics using Pig, Hive and YARN
  6. 6. Implement HBase and MapReduce integration
  7. 7. Implement Advanced Usage and Indexing
  8. 8. Schedule jobs using Oozie
  9. 9. Implement best practices for Hadoop development
  10. 10. Work on a real-life Project on Big Data Analytics
  11. 11. Understand Spark and its Ecosystem
  12. 12. Learn how to work in RDD in Spark

Who should learn this course?

Market for Big Data analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Here are the few Professional IT groups, who are continuously enjoying the benefits moving into Big data domain:

  • 1. Developers and Architects
  • 2. BI/ETL/DW professionals
  • 3. Senior IT Professionals
  • 4. Testing professionals
  • 5. Mainframe professionals
  • 6. Freshers

What are Pre-requisites for the Hadoop Course?

As such, there are no pre-requisites for learning Hadoop. Knowledge of Core Java and SQL will be beneficial, but certainly not a mandate. If you wish to brush-up Core-Java skills, TechyBees offer you a complimentary self-paced course, i.e. "Java essentials for Hadoop" when you enroll in Big Data Hadoop Certification course.

How will I get hands on experience in online training?

For projects and assignments, we will help you to setup TechyBees Virtual Machine in your system with local access. The detailed installation guide will be present in LMS for setting up the environment. In case, your system doesn't meet the pre-requisites e.g. 4GB RAM, you will be provided remote access to the TechyBees cluster for doing projects. If there is any difficulty our support team will promptly assist you. TechyBees Virtual Machine can be installed on Mac or Windows machine and the VM access will continue even after the course completed, so that you can keep practicing.

Where do our learners come from?

Professionals from around the globe have benefited from Techybees's Big Data Hadoop Certification course. Some of the top places that our learners come from include San Francisco, Bay Area, New York, New Jersey, Houston, Seattle, Toronto, London, Berlin, UAE, Singapore, Australia, New Zealand, Bangalore, New Delhi, Mumbai, Pune, Kolkata, Hyderabad and Gurgaon among many.

Techybees's Big Data Hadoop online training is one of the most sought after in the industry and has helped thousands of Big Data professionals around the globe bag top jobs in the industry. This online training includes lifetime training material access, 24X7 support for your questions, class recordings and mobile access. Our Big Data Hadoop certification also include an overview of Apache Spark for distributed data processing.

Learning Objectives - In this module, you will understand Big Data, the limitations of the existing solutions for Big Data problem, how Hadoop solves the Big Data problem, the common Hadoop ecosystem components, Hadoop Architecture, HDFS, Anatomy of File Write and Read, how MapReduce Framework works.

Topics - Big Data, Limitations and Solutions of existing Data Analytics Architecture, Hadoop, Hadoop Features, Hadoop Ecosystem, Hadoop 2.x core components, Hadoop Storage: HDFS, Hadoop Processing: MapReduce Framework, Hadoop Different Distributions.

Learning Objectives - In this module, you will learn the Hadoop Cluster Architecture, Important Configuration files in a Hadoop Cluster, Data Loading Techniques, how to setup single node and multi node Hadoop cluster.

Topics - Hadoop 2.x Cluster Architecture - Federation and High Availability, A Typical Production Hadoop Cluster, Hadoop Cluster Modes, Common Hadoop Shell Commands, Hadoop 2.x Configuration Files, Single node cluster and Multi node cluster set up Hadoop Administration.

Learning Objectives - In this module, you will understand Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS. You will understand concepts like Input Splits in MapReduce, Combiner & Partitioner and Demos on MapReduce using different data sets.

Topics - MapReduce Use Cases, Traditional way Vs MapReduce way, Why MapReduce, Hadoop 2.x MapReduce Architecture, Hadoop 2.x MapReduce Components, YARN MR Application Execution Flow, YARN Workflow, Anatomy of MapReduce Program, Demo on MapReduce. Input Splits, Relation between Input Splits and HDFS Blocks, MapReduce: Combiner & Partitioner, Demo on de-identifying Health Care Data set, Demo on Weather Data set.

Learning Objectives - In this module, you will learn Advanced MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and XML parsing.

Topics - Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format, Xml file Parsing using MapReduce.

Learning Objectives - In this module, you will learn Pig, types of use case we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, PIG running modes, PIG UDF, Pig Streaming, Testing PIG Scripts. Demo on healthcare dataset.

Topics - About Pig, MapReduce Vs Pig, Pig Use Cases, Programming Structure in Pig, Pig Running Modes, Pig components, Pig Execution, Pig Latin Program, Data Models in Pig, Pig Data Types, Shell and Utility Commands, Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Specialized joins in Pig, Built In Functions ( Eval Function, Load and Store Functions, Math function, String Function, Date Function, Pig UDF, Piggybank, Parameter Substitution ( PIG macros and Pig Parameter substitution ), Pig Streaming, Testing Pig scripts with Punit, Aviation use case in PIG, Pig Demo on Healthcare Data set.

Learning Objectives - This module will help you in understanding Hive concepts, Hive Data types, Loading and Querying Data in Hive, running hive scripts and Hive UDF.

Topics - Hive Background, Hive Use Case, About Hive, Hive Vs Pig, Hive Architecture and Components, Metastore in Hive, Limitations of Hive, Comparison with Traditional Database, Hive Data Types and Data Models, Partitions and Buckets, Hive Tables(Managed Tables and External Tables), Importing Data, Querying Data, Managing Outputs, Hive Script, Hive UDF, Retail use case in Hive, Hive Demo on Healthcare Data set.

Learning Objectives - In this module, you will understand Advanced Hive concepts such as UDF, Dynamic Partitioning, Hive indexes and views, optimizations in hive. You will also acquire in-depth knowledge of HBase, HBase Architecture, running modes and its components.

Topics - Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts, Hive Indexes and views Hive query optimizers, Hive : Thrift Server, User Defined Functions, HBase: Introduction to NoSQL Databases and HBase, HBase v/s RDBMS, HBase Components, HBase Architecture, Run Modes & Configuration, HBase Cluster Deployment.

Learning Objectives - This module will cover Advanced HBase concepts. We will see demos on Bulk Loading , Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster, why HBase uses Zookeeper.

Topics - HBase Data Model, HBase Shell, HBase Client API, Data Loading Techniques, ZooKeeper Data Model, Zookeeper Service, Zookeeper, Demos on Bulk Loading, Getting and Inserting Data, Filters in HBase.

Learning Objectives - In this module you will learn Spark ecosystem and its components, how scala is used in Spark, SparkContext. You will learn how to work in RDD in Spark. Demo will be there on running application on Spark Cluster, Comparing performance of MapReduce and Spark.

Topics - What is Apache Spark, Spark Ecosystem, Spark Components, History of Spark and Spark Versions/Releases, Spark a Polyglot, What is Scala?, Why Scala?, SparkContext, RDD.

Learning Objectives - In this module, you will understand working of multiple Hadoop ecosystem components together in a Hadoop implementation to solve Big Data problems. We will discuss multiple data sets and specifications of the project. This module will also cover Flume & Sqoop demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.

Topics - Flume and Sqoop Demo, Oozie, Oozie Components, Oozie Workflow, Scheduling with Oozie, Demo on Oozie Workflow, Oozie Co-ordinator, Oozie Commands, Oozie Web Console, Oozie for MapReduce, PIG, Hive, and Sqoop, Combine flow of MR, PIG, Hive in Oozie, Hadoop Project Demo, Hadoop Integration with Talend.

Towards the end of the course, you will work on a live project where you will be using PIG, HIVE, HBase and MapReduce to perform Big Data analytics. Following are a few industry-specific Big Data case-studies that are included in our Big Data and Hadoop Certification e.g. Finance, Retail, Media, Aviation etc. which you can consider for your project work.

Apart from these there are some twenty more use cases to choose from:

  • Market data Analysis
  • Twitter Data Analysis

Industry: Social Media

Data: It comprises of the information gathered from sites like reddit.com, stumbleupon.com which are bookmarking sites and allow you to bookmark, review, rate, search various links on any topic.reddit.com, stumbleupon.com, etc. A bookmarking site allows you to bookmark, review, rate, search various links on any topic. The data is in XML format and contains various links/posts URL, categories defining it and the ratings linked with it.

Problem Statement: Analyze the data in the Hadoop ecosystem to:

  1. Fetch the data into a Hadoop Distributed File System and analyze it with the help of MapReduce, Pig and Hive to find the top rated links based on the user comments, likes etc.
  2. Using MapReduce, convert the semi-structured format (XML data) into a structured format and categorize the user rating as positive and negative for each of the thousand links.
  3. Push the output HDFS and then feed it into PIG, which splits the data into two parts: Category data and Ratings data.
  4. Write a fancy Hive Query to analyze the data further and push the output is into relational database (RDBMS) using Sqoop.
  5. Use a web server running on grails/java/ruby/python that renders the result in real time processing on a website.

Industry: Retail

Data: Publicly available dataset, containing a few lakh observations with attributes like; CustomerId, Payment Mode, Product Details, Complaint, Location, Status of the complaint, etc.

Problem Statement: Analyze the data in the Hadoop ecosystem to:

  1. Get the number of complaints filed under each product
  2. Get the total number of complaints filed from a particular location
  3. Get the list of complaints grouped by location which has no timely response

Industry: Tourism

Data: The dataset comprises attributes like: City pair (combination of from and to), adults traveling, seniors traveling, children traveling, air booking price, car booking price, etc.

Problem Statement: Find the following insights from the data:

  1. Top 20 destinations people frequently travel to: Based on given data we can find the most popular destinations where people travel frequently, based on the specific initial number of trips booked for a particular destination
  2. Top 20 locations from where most of the trips start based on booked trip count
  3. Top 20 high air-revenue destinations, i.e the 20 cities that generate high airline revenues for travel, so that the discount offers can be given to attract more bookings for these destinations.

Industry: Aviation

Data: Publicly available dataset which contains the flight details of various airlines such as: Airport id, Name of the airport, Main city served by airport, Country or territory where airport is located, Code of Airport, Decimal degrees, Hours offset from UTC, Timezone, etc.

Problem Statement: Analyze the airlines' data to:

  1. Find list of airports operating in the country
  2. Find the list of airlines having zero stops
  3. List of airlines operating with code share
  4. Which country (or) territory has the highest number of airports
  5. Find the list of active airlines in the United States

Industry: Banking and Finance

Data: Publicly available dataset which contains complete details of all the loans issued, including the current loan status (Current, Late, Fully Paid, etc.) and latest payment information.

Problem Statement: Find the number of cases per location and categorize the count with respect to reason for taking loan and display the average risk score.

Industry: Media

Data: Publicly available data from sites like rotten tomatoes, IMDB, etc.

Problem Statement: Analyze the movie ratings by different users to:

  1. Get the user who has rated the most number of movies
  2. Get the count of total number of movies rated by user belonging to a specific occupation
  3. Get the number of underage users

Data: It is about the YouTube videos and contains attributes such as: VideoID, Uploader, Age, Category, Length, views, ratings, comments, etc.

Problem Statement: Identify the top 5 categories in which the most number of videos are uploaded, the top 10 rated videos, and the top 10 most viewed videos.

FAQ's

We will help you to setup Techybees's Virtual Machine in your System with local access. The detailed installation guides are provided in the LMS for setting up the environment. In case your system doesn't meet the pre-requisites e.g. 4GB RAM, you will be provided remote access to the Techybees cluster for the practicals. For any doubt, the 24*7 support team will promptly assist you.Techybees Virtual Machine can be installed on Mac or Windows machine.

You will never lose any lecture. You can choose either of the two options: 1. View the recorded session of the class available in your LMS. 2. You can attend the missed session, in any other live batch.

All our instructors are working professionals from the Industry and have at least 10-12 yrs of relevant experience in various domains. They are subject matter experts and are trained by Techybees for providing online training so that participants get a great learning experience.

Techybees is committed to provide you an awesome learning experience through world-class content and best-in-class instructors. We will create an ecosystem through this training, that will enable you to convert opportunities into job offers by presenting your skills at the time of an interview. We can assist you on resume building and also share important interview questions once you are done with the training. However, please understand that we are not into job placements.

You can master Hadoop, irrespective of your IT background. While basic knowledge of Core Java and SQL might help, it is not a pre-requisite for learning Hadoop. In case you wish to brush-up your Java skills, Techybees offers you a complimentary self-paced course: "Java essentials for Hadoop".

Professionals with Administration experience can take up "Hadoop Administration" course training. It will be a natural career progression. If you are planning for Big Data Architect role then you may consider both Hadoop developer and Hadoop Administration training, sequentially.

Yes, it is possible. Detailed installation guides are provided in the LMS for setting up the environment

Absolutely yes! One can always use Windows to work on Hadoop. You need to install Oracle Virtual Box on your Windows machine and then you can import Techybees Virtual Machine in it, which we will provide you.

Your system should have 4GB RAM, a processor better than core 2 duo. In case, your system falls short of these requirements, we can provide you remote access to our Hadoop Cluster.

Yes, this can be done. Moreover, this ensures that when you will start with your actual Batch, the concepts explained during the classes will not be totally new to you. Because you would have already done some preparation at your end, you will be in the position to ask the right questions and get the most out of the course.

Yes, we do schedule free demo sessions before we start any new batch. However, you can go through the sample class recordings and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.

Requesting for a support session is a very simple process. As soon as you join the course, the contact number and email-id of the support team will be available in your LMS. Just a phone call or email will solve the purpose.

These classes will be completely Online Live Instructor-led Interactive sessions. You will have chat option available to discuss your queries with instructor during a class.

Depending on the batch you select, Your Live Classes will be held either every weekend for 5 weeks or for 15 weekdays. It would typically be 6-7 hours of effort needed each week post live sessions. This shall comprise hands-on assignments.

1 Mbps of internet speed is preferable to attend the LIVE classes.

You can pay by Credit Card, Debit Card or Net Banking from all the leading banks. For USD payment, you can pay by PayPal.

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Placement Assistance :

Techybees is committed to provide you an awesome learning experience through world-class content and best-in-class instructors. We will create an ecosystem through this training, that will enable you to convert opportunities into job offers by presenting your skills at the time of an interview. We can assist you on resume building and also share important interview questions once you are done with the training. However, please understand that we are not into job placements.

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