Hadoop All in 1 and R Programming Training: Combo Course

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Key Features

  • Course Duration : 86 Hrs
  • Hands on Exercise and Project Work: 122 Hrs
  • Access Duration: Lifetime
  • 24 x 7 Support
  • Get Certified
  • Job Assistance

About Hadoop All in 1 and R Programming Training Course

In this Hadoop and R Programming training combo course, you will learn to analyze, extract and compute large volumes of structured and unstructured data using Hadoop and R Programming.

It is an all-in-one course designed to give a 360-degree overview of Hadoop architecture and its implementation on real-time projects, along with core concepts of R Programming applied to import various formats of data for statistical computing and graphics. The major topics include Hadoop and its ecosystem, core concepts of MapReduce and HDFS, introduction to HBase architecture, Hadoop Cluster Setup, Hadoop Administration and Maintenance. The course further trains you on data structures, variables, control flow, functions and getting data into the R environment, overview of statistics in R, descriptive statistics, inferential statistics, linear regression, sophisticated graphics in R, R Programming for mapping and GIS and integrating R Programming with Hadoop.

Key Features:

  • This is a combo course including:
    1. Hadoop Developer Training
    2. Hadoop Analyst Training
    3. Hadoop Administration Training
    4. Hadoop Testing Training
    5. R programming
  • 86 hours of high-quality in-depth video e-learning sessions
  • 122 hours of lab exercises
  • Intellipaat Proprietary VM for lifetime and free cloud access for 6 months for performing exercises
  • 70% of extensive learning through hands-on exercises, project works, assignments and quizzes
  • Preparing for Cloudera Spark and Hadoop Developer Certification (CCA175), Cloudera CCA Administrator Exam (CCA131) exam and R Certification exams
  • Working with Hortonworks and MapR Distributions
  • 24/7 lifetime support with guaranteed rapid problem resolution
  • Lifetime access to videos, tutorials and course material
  • Guidance to resume preparation and job assistance
  • Step-by-step installation of software
  • Course Completion Certificate from Intellipaat

Learning Objectives:

After the completion of this Hadoop all-in-one course, you will be able to:

  • Excel in the concepts of Hadoop Distributed File System (HDFS)
  • Implement HBase and MapReduce integration
  • Understand Data Science Project Life Cycle, Data Acquisition and Data Collection
  • Execute various Machine Learning Algorithms
  • Understand Apache Hadoop 2.7 framework and architecture
  • Learn to write complex MapReduce programs in both MRv1 and MRv2
  • Design and develop applications involving large data using Hadoop Ecosystem
  • Understand Prediction and Analysis Segmentation through Clustering
  • Learn the basics of Big Data and ways to integrate R with Hadoop
  • Learn various advanced modules like YARN, Flume, Hive, Oozie, Impala, ZooKeeper and Hue.
  • Set up Hadoop infrastructure with single and multi-node clusters using Amazon EC2 (CDH4)
  • Monitor a Hadoop cluster and execute routine administration procedures
  • Understand the functioning of R-Calculator
  • Master Vector Creation and assigning values to variables
  • Generate Repeats and Factor levels
  • Gain insight into database connectivity, reading data to ODBC tables, linear regression and logistic regression
  • Prepare a comprehensive case study on R Programming using Hadoop

Project Works

Hadoop Projects

1. Project – Working with MapReduce, Hive, Sqoop

Problem Statement – It describes how to import MySQL data using sqoop and querying it using hive and also describes how to run the word count MapReduce job.

2. Project – Work on Movie lens data for finding top records

Data – Movie Lens dataset

Problem Statement – It includes:

  • Write a MapReduce program to find the top 10 movies from the u.data file
  • Create the same top 10 movies using PIG by loading u.data into pig
  • Create the same top 10 movies using HIVE by loading u.data into HIVE

3. Project – Hadoop Yarn Project – End to End PoC

Problem Statement – It includes:

  • Import Movie data
  • Append the data
  • How to use sqoop commands to bring the data into the HDFS
  • End to End flow of transaction data
  • How to process the real word data or a huge amount of data using map reduce program in terms of the movie etc.

4. Project – Partitioning Tables

Problem Statement – It describes the parting and How to perform portioning. It includes:

  • Manual Partitioning
  • Dynamic Partitioning
  • Bucketing

5. Project – Sales Commission

Data – Sales

Problem Statement – In this we calculate the commission according to the sales.

6. Project – Connecting Pentaho with Hadoop Ecosystem

Problem Statement – It includes:

  • Quick Overview of ETL and BI
  • Configuring Pentaho to work with Hadoop Distribution
  • Loading data into Hadoop cluster
  • Transforming data into Hadoop cluster
  • Extracting data from Hadoop Cluster

7. Project – Multinode Cluster Setup

Problem Statement – It includes following actions:

  • Hadoop Multi-Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup
  • Running Map Reduce Jobs on Cluster

8. Project – Hadoop Testing using MR

Problem Statement – It describes that how to test map reduce codes with MR unit.

9. Project – Hadoop Weblog Analytics

Data – Weblogs

Problem Statement – The goal is to enable the participants to have a feel of the actual data sets in a production environment and how to load the data into a Hadoop cluster using various techniques. Once data is loaded, the next goal is to perform basic analytics on this data.

2. R Programming Project – Restaurant Revenue Prediction

Data – Revenue Dataset

Problem Statement – It predicts the annual restaurant sales based on the objective measurements. It uses following data fields:

  • Id
  • Opening Date
  • Type of the City
  • Type of the Restaurant
  • Three categories of Obfuscated Data
  • Revenue

It also includes:

  • Data Overview
  • Data Fields
  • Evaluation using RMSE
  • Feature Engineering / Selection


  • Basic knowledge of UNIX
  • Prior knowledge of Apache Hadoop is not required
  • Background knowledge in Statistics

Recommended Audience:

  • Programming Developers, System Administrators and ETL Developers
  • Project Managers eager to learn new techniques of maintaining large data
  • Experienced working professionals aiming to become Big Data Analysts
  • Professionals aiming to build career in real-time Data Analytics with Apache Storm techniques and Hadoop Computing
  • Professionals aspiring to be a ‘Data Scientist’
  • Information Architects to gain expertise in Predictive Analytics domain
  • Mainframe Professionals, Architects and Testing Professionals
  • Graduates eager to learn the latest Big Data technology.

Why should you take up Hadoop All in 1 and R Programming combo training?

  • This course provides an exploratory data analysis approach using concepts of R Programming and Hadoop.
  • It gives the complete study of effective data handling, amazing graphical facilities for data analytics and user-friendly ways to create top-notch graphics.
  • Big multinational companies like Google, Yahoo, Apple, eBay, Facebook and many others are hiring skilled professionals capable of handling Big Data using Hadoop and Data Science techniques.
  • The training certifies you for the biggest, top-paid job opportunities in top MNCs working on Big Data, R Programming and Hadoop.
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Big Data Hadoop Course Content

Hadoop Installation & setup

The architecture of Hadoop 2.0 cluster, what is High Availability and Federation, how to setup a production cluster, the various shell commands in Hadoop, understanding configuration files in Hadoop 2.0, installing single node cluster with Cloudera Manager, understanding Spark, Scala, Sqoop, Pig and Flume.

Introduction to Big Data Hadoop. Understanding HDFS & Mapreduce

Introducing Big Data & Hadoop, what is Big Data and where does Hadoop fits in, two important Hadoop ecosystem componentsnamely Map Reduce and HDFS, in-depth Hadoop Distributed File System – Replications, Block Size, Secondary Name node, High Availability, in-depth YARN – Resource Manager, Node Manager.

Hands-on Exercise – HDFS working mechanism, data replication process, how to determine the size of the block, understanding a DataNode and NameNode.

Deep Dive in Mapreduce

Learning the working mechanism of MapReduce, understanding the mapping and reducing stages in MR, the various terminologies in MR like Input Format, Output Format, Partitioners, Combiners, Shuffle and Sort

Hands-on Exercise – How to write a Word Count program in MapReduce, how to write a custom Partitioner, what is a MapReduce Combiner, how to run a job in a local job runner, deploying unit test, what is a map side join and reduce side join, what is a tool runner, how to use counters, dataset joining with map side and reduce side joins.

Introduction to Hive

Introducing Hadoop Hive, detailed architecture of Hive, comparing Hive with Pig and RDBMS, working with Hive Query Language, creation of database, table, Group by and other clauses, the various types of Hive tables, Hcatalog, storing the Hive Results, Hive partitioning and Buckets.

Hands-on Exercise – Database creation in Hive, dropping a database, Hive table creation, how to change the database, data loading, Hive table creation, dropping and altering table, pulling data by writing Hive queries with filter conditions, table partitioning in Hive, what is a group by clause

Advance Hive & Impala

The indexing in Hive, the Map side Join in Hive, working with complex data types, the Hive User-defined Functions, Introduction to Impala, comparing Hive with Impala, the detailed architecture of Impala

Hands-on Exercise – How to work with Hive queries, the process of joining table and writing indexes, external table and sequence table deployment, data storage in a different table.

Introduction to Pig

Apache Pig introduction, its various features, the various data types and schema in Hive, the available functions in Pig, Hive Bags, Tuples and Fields.

Hands-on Exercise – Working with Pig in MapReduce and local mode, loading of data, limiting data to 4 rows, storing the data into file, working with Group By,Filter By,Distinct,Cross,Split in Hive.

Flume, Sqoop & HBase

Apache Sqoop introduction, overview, importing and exporting data, performance improvement with Sqoop, Sqoop limitations, introduction to Flume and understanding the architecture of Flume, what is HBase and the CAP theorem.

Hands-on Exercise – Working with Flume to generating of Sequence Number and consuming it, using the Flume Agent to consume the Twitter data, using AVRO to create Hive Table, AVRO with Pig, creating Table in HBase, deploying Disable, Scan and Enable Table.

Writing Spark Applications using Scala

Using Scala for writing Apache Spark applications, detailed study of Scala, the need for Scala, the concept of object oriented programing, executing the Scala code, the various classes in Scala like Getters,Setters, Constructors, Abstract ,Extending Objects, Overriding Methods, the Java and Scala interoperability, the concept of functional programming and anonymous functions, Bobsrockets package, comparing the mutable and immutable collections.

Hands-on Exercise – Writing Spark application using Scala, understanding the robustness of Scala for Spark real-time analytics operation.

Spark framework

Detailed Apache Spark, its various features, comparing with Hadoop, the various Spark components, combining HDFS with Spark, Scalding, introduction to Scala, importance of Scala and RDD.

Hands-on Exercise – The Resilient Distributed Dataset in Spark and how it helps to speed up big data processing.

RDD in Spark

Understanding the Spark RDD operations, comparison of Spark with MapReduce, what is a Spark transformation, loading data in Spark, types of RDD operations viz. transformation and action, what is Key Value pair.

Hands-on Exercise – How to deploy RDD with HDFS, using the in-memory dataset, using file for RDD, how to define the base RDD from external file, deploying RDD via transformation, using the Map and Reduce functions, working on word count and count log severity.

Data Frames and Spark SQL

The detailed Spark SQL, the significance of SQL in Spark for working with structured data processing, Spark SQL JSON support, working with XML data, and parquet files, creating HiveContext, writing Data Frame to Hive,  How to read a JDBC file, significance of a Spark Data Frame, how to create a Data Frame, what is schema manual inferring, how to work with CSV files, JDBC table reading, data conversion from Data Frame to JDBC, Spark SQL user-defined functions. shared variable and accumulators, how to query and transform data in Data Frames, how Data Frame provides the benefits of both Spark RDD and Spark SQL, deploying Hive on Spark as the execution engine.

Hands-on Exercise – Data querying and transformation using Data Frames, finding out the benefits of Data Frames over Spark SQL and Spark RDD.

Machine Learning using Spark (Mlib)

Introduction to Spark MLlib, understanding the various algorithms, what is Spark iterative algorithm, Spark graph processing analysis, introducing machine learning, K-Means clustering, Spark variables like shared and broadcast variables, what are accumulators.

Hands-on Exercise – Writing spark code using Mlib.

Spark Streaming

Introduction to Spark streaming, the architecture of Spark Streaming, working with the Spark streaming program, processing data using Spark streaming, requesting count and Dstream, multi-batch and sliding window operations and working with advanced data sources.

Hands-on Exercise – Deploying Spark streaming for data in motion and checking the output is as per the requirement.

Hadoop Administration – Multi Node Cluster Setup using Amazon EC2

Create a four node Hadoop cluster setup, running the MapReduce Jobs on the Hadoop cluster, successfully running the MapReduce code, working with the Cloudera Manager setup.

Hands-on Exercise – The method to build a multi-node Hadoop cluster using an Amazon EC2 instance, working with the Cloudera Manager.

Hadoop Administration – Cluster Configuration

The overview of Hadoop configuration, the importance of Hadoop configuration file, the various parameters and values of configuration, the HDFS parameters and MapReduce parameters, setting up the Hadoop environment, the Include’ and Exclude configuration files, the administration and maintenance of Name node, Data node directory structures and files, What is a File system image, understanding Edit log.

Hands-on Exercise – The process of performance tuning in MapReduce.

Hadoop Administration – Maintenance, Monitoring and Troubleshooting

Introduction to the Checkpoint Procedure, Name node failure and how to ensure the recovery procedure, Safe Mode, Metadata and Data backup, the various potential problems and solutions, what to look for, how to add and remove nodes.

Hands-on Exercise – How to go about ensuring the MapReduce File system Recovery for various different scenarios, JMX monitoring of the Hadoop cluster, how to use the logs and stack traces for monitoring and troubleshooting, using the Job Scheduler for scheduling jobs in the same cluster, getting the MapReduce job submission flow, FIFO schedule, getting to know the Fair Scheduler and its configuration.

ETL Connectivity with Hadoop Ecosystem

How ETL tools work in Big data Industry, Introduction to ETL and Data warehousing. Working with prominent use cases of Big data in ETL industry, End to End ETL PoC showing big data integration with ETL tool.

Hands-on Exercise – Connecting to HDFS from ETL tool and moving data from Local system to HDFS, Moving Data from DBMS to HDFS, Working with Hive with ETL Tool, Creating Map Reduce job in ETL tool

Project Solution Discussion and Cloudera Certification Tips & Tricks

Working towards the solution of the Hadoop project solution, its problem statements and the possible solution outcomes, preparing for the Cloudera Certifications, points to focus for scoring the highest marks, tips for cracking Hadoop interview questions.

Hands-on Exercise – The project of a real-world high value Big Data Hadoop application and getting the right solution based on the criteria set by the Intellipaat team.

Following topics will be available only in self-paced Mode.

Hadoop Application Testing

Why testing is important, Unit testing, Integration testing, Performance testing, Diagnostics, Nightly QA test, Benchmark and end to end tests, Functional testing, Release certification testing, Security testing, Scalability Testing, Commissioning and Decommissioning of Data Nodes Testing, Reliability testing, Release testing

Roles and Responsibilities of Hadoop Testing Professional

Understanding the Requirement, preparation of the Testing Estimation, Test Cases, Test Data, Test bed creation, Test Execution, Defect Reporting, Defect Retest, Daily Status report delivery, Test completion, ETL testing at every stage (HDFS, HIVE, HBASE) while loading the input (logs/files/records etc) using sqoop/flume which includes but not limited to data verification, Reconciliation, User Authorization and Authentication testing (Groups, Users, Privileges etc), Report defects to the development team or manager and driving them to closure, Consolidate all the defects and create defect reports, Validating new feature and issues in Core Hadoop.

Framework called MR Unit for Testing of Map-Reduce Programs

Report defects to the development team or manager and driving them to closure, Consolidate all the defects and create defect reports, Responsible for creating a testing Framework called MR Unit for testing of Map-Reduce programs.

Unit Testing

Automation testing using the OOZIE, Data validation using the query surge tool.

Test Execution

Test plan for HDFS upgrade, Test automation and result

Test Plan Strategy and writing Test Cases for testing Hadoop Application

How to test install and configure

R Programming Course Content

Introduction to R

R language for statistical programming, the various features of R, introduction to R Studio, the statistical packages, familiarity with different data types and functions, learning to deploy them in various scenarios, use SQL to apply ‘join’ function, components of R Studio like code editor, visualization and debugging tools, learn about R-bind.


R Functions, code compilation and data in well-defined format called R-Packages, learn about R-Package structure, Package metadata and testing, CRAN (Comprehensive R Archive Network), Vector creation and variables values assignment.

Sorting Dataframe

R functionality, Rep Function, generating Repeats, Sorting and generating Factor Levels, Transpose and Stack Function.

Matrices and Vectors

Introduction to matrix and vector in R, understanding the various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions.

Reading data from external files

Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists, understanding how to read data from external files.

Generating plots

Generate plot in R, Graphs, Bar Plots, Line Plots, Histogram, components of Pie Chart.

Analysis of Variance (ANOVA)

Understanding Analysis of Variance (ANOVA) statistical technique, working with Pie Charts, Histograms, deploying ANOVA with R, one way ANOVA, two way ANOVA.

K-means Clustering

K-Means Clustering for Cluster & Affinity Analysis, Cluster Algorithm, cohesive subset of items, solving clustering issues, working with large datasets, association rule mining affinity analysis for data mining and analysis and learning co-occurrence relationships.

Association Rule Mining

Introduction to Association Rule Mining, the various concepts of Association Rule Mining, various methods to predict relations between variables in large datasets, the algorithm and rules of Association Rule Mining, understanding single cardinality.

Regression in R

Understanding what is Simple Linear Regression, the various equations of Line, Slope, Y-Intercept Regression Line, deploying analysis using Regression, the least square criterion, interpreting the results, standard error to estimate and measure of variation.

Analyzing Relationship with Regression

Scatter Plots, Two variable Relationship, Simple Linear Regression analysis, Line of best fit

Advance Regression

Deep understanding of the measure of variation, the concept of co-efficient of determination, F-Test, the test statistic with an F-distribution, advanced regression in R, prediction linear regression.

Logistic Regression

Logistic Regression Mean, Logistic Regression in R.

Advance Logistic Regression

Advanced logistic regression, understanding how to do prediction using logistic regression, ensuring the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system, ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier.

Receiver Operating Characteristic (ROC)

Detailed understanding of ROC, area under ROC Curve, converting the variable, data set partitioning, understanding how to check for multicollinearlity, how two or more variables are highly correlated, building of model, advanced data set partitioning, interpreting of the output, predicting the output, detailed confusion matrix, deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates.

Kolmogorov Smirnov Chart

Data analysis with R, understanding the WALD test, MC Fadden’s pseudo R-squared, the significance of the area under ROC Curve, Kolmogorov Smirnov Chart which is non-parametric test of one dimensional probability distribution.

Database connectivity with R

Connecting to various databases from the R environment, deploying the ODBC tables for reading the data, visualization of the performance of the algorithm using Confusion Matrix.

Integrating R with Hadoop

Creating an integrated environment for deploying R on Hadoop platform, working with R Hadoop, RMR package and R Hadoop Integrated Programming Environment, R programming for MapReduce jobs and Hadoop execution.

R Case Studies

Logistic Regression Case Study

In this case study you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns, uncover insights and more all through the power of R programming. Due to this the future advertisement spends can be decided and optimized for higher revenues.

Multiple Regression Case Study

You will understand how to compare the miles per gallon (MPG) of a car based on the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostic, checking the ROC curve, among other things.

Receiver Operating Characteristic (ROC) case study

You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real world data, check the ROC curve and more.

What Hadoop Projects You will be working on?

Project 1 : Working with MapReduce, Hive, Sqoop
Industry : General
Problem Statement :  How to successfully import data using Sqoop into HDFS for data analysis.
Topics :  As part of this project you will work on the various Hadoop components like MapReduce, Apache Hive and Apache Sqoop. Work with Sqoop to import data from relational database management system like MySQL data into HDFS. Deploy Hive for summarizing data, querying and analysis. Convert SQL queries using HiveQL for deploying MapReduce on the transferred data. You will gain considerable proficiency in Hive, and Sqoop after completion of this project.

Highlights :

  • Sqoop data transfer from RDBMS to Hadoop
  • Coding in Hive Query Language
  • Data querying and analysis.
Project 2 : Work on MovieLens data for finding top movies
Industry : Media and Entertainment
Problem Statement :  How to create the top ten movies list using the MovieLens data.
Topics :  In this project you will work exclusively on data collected through MovieLens available rating data sets. The project involves writing MapReduce program to analyze the MovieLens data and create list of top ten movies. You will also work with Apache Pig and Apache Hive for working with distributed datasets and analyzing it.

Highlights :

  • MapReduce program for working on the data file
  • Apache Pig for analyzing data
  • Apache Hive data warehousing and querying
Project 3 : Hadoop YARN Project – End to End PoC
Industry : Banking
Problem Statement :  How to bring the daily data ( incremental data) into the Hadoop Distributed File System.
Topics :  In this project we have transaction data which is daily recorded/store in the RDBMS. Now this data is transferred everyday into HDFS for further Big Data Analytics. You will work on live Hadoop YARN cluster. YARN is part of the Hadoop 2.0 ecosystem that lets Hadoop to decouple from MapReduce and deploy more competitive processing and wider array of applications. You will work on the YARN central Resource Manager.

Highlights :

  • Using Sqoop commands to bring the data into HDFS
  • End to End flow of transaction data
  • Working with the data from HDFS
Project 4 : Table Partitioning in Hive
Industry : Banking
Problem Statement :  How to improve the query speed using Hive data partitioning.
Topics :  This project involves working with Hive table data partitioning. Ensuring the right partitioning helps to read the data, deploy it on the HDFS, and run the MapReduce jobs at a much faster rate. Hive lets you partition data in multiple ways. This will give you hands-on experience in partitioning of Hive tables manually, deploying single SQL execution in dynamic partitioning, bucketing of data so as to break it into manageable chunks.

Highlights :

  • Manual Partitioning
  • Dynamic Partitioning
  • Bucketing
Project 5 : Connecting Pentaho with Hadoop Ecosystem
Industry : Social Network
Problem Statement :  How to deploy ETL for data analysis activities.
Topics :  This project lets you connect Pentaho with the Hadoop ecosystem. Pentaho works well with HDFS, HBase, Oozie and Zookeeper. You will connect the Hadoop cluster with Pentaho data integration, analytics, Pentaho server and report designer. This project will give you complete working knowledge on the Pentaho ETL tool.

Highlights :

  • Working knowledge of ETL and Business Intelligence
  • Configuring Pentaho to work with Hadoop Distribution
  • Loading, Transforming and Extracting data into Hadoop cluster
Project 6 : Multi-node cluster setup
Industry : General
Problem Statement :  How to setup a Hadoop real-time cluster on Amazon EC2.
Topics :  This is a project that gives you opportunity to work on real world Hadoop multi-node cluster setup in a distributed environment. You will get a complete demonstration of working with various Hadoop cluster master and slave nodes, installing Java as a prerequisite for running Hadoop, installation of Hadoop and mapping the nodes in the Hadoop cluster.

Highlights :

  • Hadoop installation and configuration
  • Running a Hadoop multi-node using a 4 node cluster on Amazon EC2
  • Deploying of MapReduce job on the Hadoop cluster.
Project 7 : Hadoop Testing using MRUnit
Industry : General
Problem Statement :  How to test MapReduce applications
Topics :  In this project you will gain proficiency in Hadoop MapReduce code testing using MRUnit. You will learn about real world scenarios of deploying MRUnit, Mockito, and PowerMock. This will give you hands-on experience in the various testing tools for Hadoop MapReduce. After completion of this project you will be well-versed in test driven development and will be able to write light-weight test units that work specifically on the Hadoop architecture.

Highlights :

  • Writing JUnit tests using MRUnit for MapReduce applications
  • Doing mock static methods using PowerMock & Mockito
  • MapReduce Driver for testing the map and reduce pair
Project 8 : Hadoop Weblog Analytics
Industry : Internet services
Problem Statement :  How to derive insights from web log data
Topics :  This project is involved with making sense of all the web log data in order to derive valuable insights from it. You will work with loading the server data onto a Hadoop cluster using various techniques. The web log data can include various URLs visited, cookie data, user demographics, location, date and time of web service access, etc. In this project you will transport the data using Apache Flume or Kafka, workflow and data cleansing using MapReduce, Pig or Spark. The insight thus derived can be used for analyzing customer behavior and predict buying patterns.

Highlights :

  • Aggregation of log data
  • Apache Flume for data transportation
  • Processing of data and generating analytics
Project 9 : Hadoop Maintenance
Industry : General
Problem Statement :  How to administer a Hadoop cluster
Topics :  This project is involved with working on the Hadoop cluster for maintaining and managing it. You will work on a number of important tasks that include recovering of data, recovering from failure, adding and removing of machines from the Hadoop cluster and onboarding of users on Hadoop.

Highlights :

  • Working with name node directory structure
  • Audit logging, data node block scanner, balancer.
  • Failover, fencing, DISTCP, Hadoop file formats.
Project 10 : Twitter Sentiment Analysis
Industry – Social Media
Problem Statement :  Find out what is the reaction of the people to the demonetization move by India by analyzing their tweets.
Description :  This Project involves analyzing the tweets of people by going through what they are saying about the demonetization decision taken by the Indian government. Then you look for key phrases, words and analyze them using the dictionary and the value attributed to them based on the sentiment that it is conveying.

Highlights :

  • Download the Tweets & Load into Pig Storage
  • Divide tweets into words to calculate sentiment
  • Rating the words from +5 to -5 on AFFIN dictionary
  • Filtering the Tweets and analyzing sentiment.
Project 11 :  Analyzing IPL T20 Cricket
Industry  –  Sports & Entertainment
Problem Statement :  Analyze the entire cricket match and get answers to any question regarding the details of the match.
Description :  This project involves working with the IPL dataset that has information regarding batting, bowling, runs scored, wickets taken, and more. This dataset is taken as input and then it is processed so that the entire match can be analyzed based on the user queries or needs.

Highlights :

  • Load the data into HDFS
  • Analyze the data using Apache Pig or Hive
  • Based on user queries give the right output

Apache Spark Projects

Project 1 – Movie Recommendation
Industry : Entertainment
Problem Statement :  How to recommend the most appropriate movie to a user based on his taste
Topics :This is a hands-on Apache Spark project deployed for the real-world application of movie recommendations. This project helps you gain essential knowledge in Spark MLlib which is a machine learning library, you will know how to create collaborative filtering, regression, clustering and dimensionality reduction using Spark MLlib. Upon finishing the project you will have first-hand experience in the Apache Spark streaming data analysis, sampling, testing, and statistics among other vital skills.

Highlights :

  • Apache Spark MLlib component
  • Statistical analysis
  • Regression & clustering
Project 2 –Twitter API Integration for tweet Analysis
Industry : Social Media
Problem Statement :  Analyzing the user sentiment based on the tweet
Topics :This is a hands-on Twitter analysis project using the Twitter API for analyzing of tweets. You will integrate the Twitter API and do programing using Python or PHP for developing the essential server side codes. Finally you will be able to read the results for various operations by filtering, parsing, and aggregating it depending on the tweet analysis requirement.

Highlights :

  • Making requests to Twitter API
  • Building the server side codes
  • Filtering, parsing & aggregating data
Project 3 –Data Exploration Using Spark SQL – Wikipedia data set
Industry : Internet
Problem Statement :  Making sense of Wikipedia data using Spark SQL.
Topics :In this project you will be using the Spark SQL tool for analyzing the Wikipedia data. You will gain hands-on experience in integrating Spark SQL for various applications like batch analysis, machine learning, visualizing and processing of data, ETL processes along with real-time analysis of data.

Highlights :

  • Machine learning using Spark
  • Deploying data visualization
  • Spark SQL integration

R Programming Projects

Project 1

Domain – Restaurant Revenue Prediction

Data set – Sales

Project Description – This project involves predicting the sales of a restaurant on the basis of certain objective measurements. This project will give real time industry experience on handling multiple use cases and derive the solution. This project gives insights about feature engineering and selection.

Project 2

Domain – Data Analytics

Objective – To predict about the class of a flower using its petal’s dimensions

Project 3

Domain – Finance

Objective – The project aims to find the most impacting factors in preferences of pre-paid model, also identifies which are all the variables highly correlated with impacting factors

Project 4

Domain – Stock Market

Objective – This project focuses on Machine Learning by creating predictive data model to predict future stock prices

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Sample Hadoop and R Programming Training Video Tutorials

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Hadoop and R Certification

This course is designed for clearing Cloudera Spark and Hadoop Developer Certification (CCA175), Cloudera CCA Administrator Exam (CCA131) and R Certification exams. At the end of the course, there will be a quiz and project assignments. Once you complete them, you will be awarded with Intellipaat Course Completion Certificate.

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Big Data Hadoop Training Reviews

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  1. Profile photo of gabehall34@yahoo.com Intellipaat Gabe Hellmat 

    Result-oriented course

    Great Job! Good introduction to uses of Hadoop and R Programming. Videos are of high quality. Thank you so much for this amazing combo.

  2. Daniel 

    Great site for IT world

    Hello :) This site is great! I am currently learning IT Software Engineering and the courses offered here help me a lot! Thanks...

  3. Kristina 

    Clear and concise content

    Tutorials, labs and instructions are clear and concise. Many thanks for making this available.

  4. Profile photo of Matt Peter Peter 

    Useful overview

    I think, it was a very useful overview of the technology related to Hadoop...

  5. David 

    Result-oriented Online Training

    Great web training, fast response and complete functionalities to support students to achieve maximum results.

Frequently Asked Questions on Hadoop and R Programming Training

What are the different modes of training that Intellipaat provides?
At Intellipaat you can enroll either for the instructor-led online training or self-paced training. Apart from this Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience and they have been actively working as consultants in the same domain making them subject matter experts. Go through the sample videos to check the quality of the trainers.
Can I request for a support session if I need to better understand the topics?
Intellipaat is offering the 24/7 query resolution and you can raise a ticket with the dedicated support team anytime. You can avail the email support for all your queries. In the event of your query not getting resolved through email we can also arrange one-to-one sessions with the trainers. You would be glad to know that you can contact Intellipaat support even after completion of the training. We also do not put a limit on the number of tickets you can raise when it comes to query resolution and doubt clearance.
Can you explain the benefits of the Intellipaat self-paced training?
Intellipaat offers the self-paced training to those who want to learn at their own pace. This training also affords you the benefit of query resolution through email, one-on-one sessions with trainers, round the clock support and access to the learning modules or LMS for lifetime. Also you get the latest version of the course material at no added cost. The Intellipaat self-paced training is 75% lesser priced compared to the online instructor-led training. If you face any problems while learning we can always arrange a virtual live class with the trainers as well.
What kind of projects are included as part of the training?
Intellipaat is offering you the most updated, relevant and high value real-world projects as part of the training program. This way you can implement the learning that you have acquired in a real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning and practical knowledge thus making you completely industry-ready. You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. Upon successful completion of the projects your skills will be considered equal to six months of rigorous industry experience.
Does Intellipaat offer job assistance?
Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this we are exclusively tied-up with over 80 top MNCs from around the world. This way you can be placed in outstanding organizations like Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation part as well.
Is it possible to switch from self-paced training to instructor-led training?
You can definitely make the switch from self-paced to online instructor-led training by simply paying the extra amount and joining the next batch of the training which shall be notified to you specifically.
How are Intellipaat verified certificates awarded?
Once you complete the Intellipaat training program along with all the real-world projects, quizzes and assignments and upon scoring at least 60% marks in the qualifying exam; you will be awarded the Intellipaat verified certification. This certificate is very well recognized in Intellipaat affiliate organizations which include over 80 top MNCs from around the world which are also part of the Fortune 500 list of companies.
Will The Job Assistance Program Guarantee Me A Job?
In our Job Assistance program we will be helping you land in your dream job by sharing your resume to potential recruiters and assisting you with resume building, preparing you for interview questions. Intellipaat training should not be regarded either as a job placement service or as a guarantee for employment as the entire employment process will take part between the learner and the recruiter companies directly and the final selection is always dependent on the recruiter.
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