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Big Data Assignment Help

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Big Data Assignment Help

Codersarts  is top rated website for  Big Data Project, Assignments and Homework Help site. Our dedicated team of Big data assignment expert will help and will guide you throughout your learning Big data analytics journey.


Big Data is a massive volume of both structured and unstructured data that is so large that it's difficult to process using traditional database and software techniques. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. and there is nothing wrong or unusual to look for assignment help to deal with it. If you come to Codersarts you will quickly find all the answers you need. Leaning Big data analytics is one of the top priorities of many students at the university. 


Hence, you might find yourself in a situation where you need help with Big data assignment. The programming part is always convoluted, and it keeps students puzzled. That is why has appointed the best programming experts to assist you with big data analytics assignments. ​

Analytics Flow for Big Data

  • Data collection

  • Data preparation

  • Analysis types (Basic Statistics, Regression, Classification, Clustering ...)

  • Analytics modes (Batch, Interactive, Real-time)

  • Visualizations (Static, Dynamic, Interactive)

Big Data Analytics  flow Codersarts.png

Big Data Analysis Tools and Frameworks

  • Tableau

  • Spark

  • SAS Studio

  • Map Reduce

  • Hadoop

  • Hive
  • Pig

  • Plotly

  • Weka

  • Storm

  • Cloudera

  • Openrefine

  • Rapidminer

  • DataCleaner

  • ​Azure HDInsight(Spark & Cloud service)

  • Azure Machine Studio

  • Azure Cognitive services

Apache Spark  Assignment Help


Apache Spark is a framework for developing distributed computing applications.

  • Apache Spark is an open-source cluster-computing framework

  • Batch and Real-time data processing with huge data

  • Originally developed at the University of California, Berkeley’s AMPLab

  • Later donated to Apache Software Foundation

  • Native integration with Java, Python, Scala.

  • More general than MapReduce which represents one set of supported constructs.​

Apache Spark Core

  • Spark SQL

  • Spark Streaming

  • MLlib (machine learning)

  • GraphX (graph)​

Features of Apache Spark

  • Speed (in-memory computations)

  • Supports multiply languages (Java, Scala, Python,R)

  • Advanced Analytics (SQL queries, Streaming data, Machine learning and Graph algorithms )​​

Who uses Spark and Why?

Data Scientist:

  • Analyze and model the data to obtain insights of the data;

  • Transforming the data into a useable format

  • Statistics, machine learning, SQL

  • Advanced analytics


  • Develop a data processing system or applications

  • Monitor, inspect and tune the applications

Real-time Analytics Assignment Help

Big data analytics uses Spark Streaming Library (Spark Streaming + Mllib) to perform real-time analytics.

Historical Data Analysis with Mllib:

  • Data Representation

  • Clustering tweets by text etc.)

  • Classification of tweets by sentiment (negative, positive, etc.)

  • Result visualization in Zepplin

Streaming Data Analysis:

  • Stream tweets in json file

  • Stream tweets to MongoDB

Big Data Processing  Assignment Help with Amazon EMR

Screenshot 2020-09-06 at 5.28.08 PM.png

Amazon EMR is a managed cluster platform that simplifies running big data frameworks, such as Apache Hadoop and Apache Spark, on AWS to process and analyze vast amounts of data. By using these frameworks and related open-source projects, such as Apache Hive and Apache Pig, you can process data for analytics purposes and business intelligence workloads.


Additionally, you can use Amazon EMR to transform and move large amounts of data into and out of other AWS data stores and databases, such as Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB.

If you are a first-time user of Amazon EMR, we recommend that you begin by reading the following terms:

  • Amazon EMR – This service page provides the Amazon EMR highlights, product details, and pricing information.

  • Getting Started: Analyzing Big Data with Amazon EMR 

This topic provides an overview of Amazon EMR clusters, including how to submit work to a cluster, how that data is processed, and the various states that the cluster goes through during processing.

Common tasks involve in big data processing with Amazon EMR are: 

  • Understanding Clusters and Nodes

  • Submitting Work to a Cluster

  • Processing Data 

  • Understanding the Cluster Lifecycle

MapReduce  Assignment Help

Hadoop is a data management and distributed processing system. It contains many components, including:

  • HDFS is a file system that distributes data across many machines

  • MapReduce for batch parallel computing


MapReduce has two main tasks:

  • Map

  • Reduce


The data blocks distributed across different machines are processed by Map tasks in parallel.

Results are aggregated in Reducers. Works only with KEY/VALUE pairs

​MapReduce Key/Value Pairs

The data exchanged between Map and Reduce, and more, in the entire job are pairs (key, value):

  • a key: it is any type of data: integer, text. . .

  • a value: it is any type of data

It is this notion that makes programs quite strange to beginners: the two functions Map and Reduce receive and transmit such pairs.

Everything is represented like this. For example :

  • a text file is a set of (line number, line).

  • a weather file is a set of (date and time, temperature)

MapReduce – Word count example


Domain Specific Examples of Big Data

Financial (Credit Risk Modeling, Fraud Detection)


  • Credit Risk Modeling: Banking and Financial institutions use credit risk modeling to score credit applications and predict if a borrower will default or not in the future. Big data systems can be used for building credit models.

Web (Web Analytics, Content Recommendation)


  • Content Recommendation: Such applications can leverage big data systems for recommending new content to the users based on user preferences and interests.

​Environment (Weather Monitoring, Air Pollution Monitoring, Noise Pollution Monitoring, Forest Fire Detection, River Floods Detection)
  • Air Pollution Monitoring: Air pollution monitoring systems can monitor the emission of harmful gases by factories and automobiles using gaseous and meteorological sensors. The collected data can be analyzed to make informed decisions on pollution control approaches.

Healthcare (Epidemiological Surveillance, Real-time health monitoring)
  • Real-time health monitoring: Big data systems for real-time data analysis can be used for the analysis of large volumes of fast-moving data from wearable devices and other in-hospital or in-home devices, for real-time patient health monitoring and adverse event prediction.

Internet of Things (Intrusion Detection, Smart Parkings, Smart Irrigation)
  • Intrusion Detection: Intrusion detection systems use security cameras and sensors (such as PIR sensors and door sensors) to detect intrusions and raise alerts.

  • Smart Parking: In smart parking, sensors are used for each parking slot, to detect whether the slot is empty or occupied. This information is aggregated by an on-site smart parking controller and then sent over the Internet to a cloud-based big data analytics backend.

Logistics & Transportation (Shipment Monitoring, Route Generation & Scheduling)
  • Shipment Monitoring: containers carrying fresh food products can be monitored to detect spoilage of food. Shipment monitoring systems use sensors such as temperature, pressure, humidity, for instance, to monitor the conditions inside the

  • containers and send the data to the cloud, where it can be analyzed to detect food spoilage using Cloud-big data analytics.

Retail (Customer Recommendations, Store Layout Optimization, Forecasting Demand)

Retailers can use big data systems for boosting sales.

  • Customer Recommendations: Big data systems can be used to analyze customer data (such as demographic data, shopping history, or customer feedback) and predict the customer preferences.

  • Store layout optimization: Big data systems can help in analyzing the data on customer shopping patterns and customer feedback to optimize the store layouts.

  • Forecasting Demand: Big data systems can be used to analyze customer purchase patterns and predict demand and sale volumes.

Industry (Machine Diagnosis and Prognosis, Production Planning and Control)
  • Production Planning and Control: Production planning and control systems measure various parameters of production processes and control the entire production process in real-time. These systems use various sensors to collect data on the production processes. Big data systems can be used to analyze this data for production planning and identifying potential problems.

Industry use/ applications

  • Crime prediction

  • Disease prediction

  • Simulating and predicting traffic patterns

  • Modeling Natural Language

  • Fraud Detection

  • Sentiment analysis of big data

  • Deceit recognition

  • Predicting the interests of audiences

  • Optimized or on-demand scheduling of media streams in digital media distribution platforms

  • Getting insights from customer reviews

  • Effective targeting of the advertisements

  • Social Media Data Analysis with Spark

  • Machine Learning Algorithms (Apply classification to Tweets)

  • Real time analysis of Tweets (Spark Streaming Library)

Applications of Big data on Education:

  • Customized and Dynamic Learning Programs: Customized programs and schemes to benefit individual students can be created using the data collected on the bases of each student’s learning history. This improves the overall student results.

  • Reframing Course Material: Reframing the course material according to the data that is collected on the basis of what a student learns and to what extent by real-time monitoring of the components of a course is beneficial for the students.

  • Grading Systems: New advancements in grading systems have been introduced as a result of a proper analysis of student data. 

  • Career Prediction: Appropriate analysis and study of every student’s records will help understand each student’s progress, strengths, weaknesses, interests, and more. It would also help in determining which career would be the most suitable for the student in future.


The applications of big data have provided a solution to one of the biggest pitfalls in the education system, that is, the one-size-fits-all fashion of academic set-up, by contributing in e-learning solutions.

Case Studies

AETNA: Looks at patient results on a series of metabolic syndrome-detecting tests, assesses patient risk factors and focuses on treating one or two things that will have the most impact (statistically speaking) on improving their health. 90% of patients who didn’t have a previous visit with their doctor would benefit from a screening, and 60% would benefit from improving their adherence to their medicine regimen.There are many pre-trained models available on the internet which could be used to extract features from the dataset in case the amount of data available is low.

AMERICAN EXPRESS: Starts looking for indicators that could predict loyalty and developed sophisticated predictive models to analyze historical transactions and 115 variables to forecast potential churn. The company believes it can now identify 24% of accounts that will close within the next four months.

ATLANTA FALCONS: Use GPS technology to assess player movements during practices, which helps the coaches create more efficient plays.


BANK OF AMERICA: “BankAmeriDeals” provides cash-back offers to credit and debit-card customers based upon analyses of their prior purchases.


BASIS: Is a wrist-based health tracker and online personal dashboard that helps users incorporate small, progressive health changes over time—that ultimately add up to major results.


BRITISH AIRWAYS: “Know Me” program combines already existing loyalty information with the data collected from customers based on their online behavior. With the blending of these two sources of information, British Airways can make more targeted offers while responding to service lapses in ways to create a more positive experience for the flyer.


CAESARS ENTERTAINMENT: Combines patrons’ gambling outcomes with their rewards program information to offer enticing perks to those who are losing at the tables.


CATAPULT: Uncovers vitally important information like whether an athlete is developing an injury, or whether certain workouts are overly stressful. That helps teams keep their players safe and game-ready. Sales grew 64% last year and Catapult now works with nearly half of NFL teams, a third of NBA teams, and 30 major college programs.


COMMONBOND: Is a student lending platform that connects students and graduates to alumni investors and accomplished professionals. Thus, students can access lower, fixed-rate financing—and save thousands of dollars on their repayments.


DELTA: With over 130 million bags checked per year, Delta has a lot of tracking data about bags and became the first major airline to allow customers to track their bags from mobile devices. To date, the app has been downloaded over 11 million times and gives customers much greater peace of mind.


DUETTO: Makes it easier for companies to personalize data to individuals searching online for hotels. Prices by hotels can be personalized by taking data such as how much you typically spend at the bar or casino to incentivize you with a lower price for your room. The hotel can give you a better price, knowing you’ll spend money on other services.


EBAY: “the Feed” is a new homepage that allows customers to follow entire categories of items no matter how obscure. This makes it easier for customers to stay on top of the latest items they have a particular interest, especially if they are collectors.


EVOVL: Helps large global companies make better hiring and management decisions through tpredictive analytics.  Evolv crunches more than 500 million data points on gas prices, unemployment rates, and social media usage to help clients like Xerox—who has cut attrition by 20 percent—predict, for example, when an employee is most likely to leave his job. Companies like Xerox, AT&T and Kelly Services use Evolv, and on average, our clients see a $10 million impact on their P&L. Evolv’s sales grew a whopping 150% from Q3 2012 to Q3 2013.


GENERAL ELECTRIC: Many machines—everything from power plants to locomotives to hospital equipment—now pump out data about how they’re operating. GE’s analytics team crunches it, then rejiggers machines to be more efficient. Even tiny improvements are substantial, given the scale: By GE’s estimates, data can boost productivity in the U.S. by 1.5%, which over a 20-year period could save enough cash to raise average national incomes by as much as 30%.


GOOGLE: Working with the U.S. Centers for Disease Control, tracks when users are inputting search terms related to flu topics, to help predict which regions may experience outbreaks.


HOMER: Handcrafted by top literacy experts, helps children learn to read. It has a complete phonics program, a library of beautifully illustrated stories, hundreds of science field trips, and exciting art and recording tools—combining the best early learning techniques into an engaging app that connects learning to read with learning to understand the world.


IRS: Uses Big Data to stop identity theft, fraud, and improper payments, such as those who are not paying taxes and should. The system also helps to ensure compliance with tax rules and laws. So far, the IRS has stopped billions of dollars in fraud, specifically with identity theft, and recovered more than $2 billion over the last three years.

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