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Ever since the inception of the internet, data has been accumulating and it has seen a boom in recent years, owing to many factors, such as the availability of cheap smartphones, and also decreasing internet prices. This has given rise to a completely new field of data science called Big Data, and projects based on Big data are known as big data projects.
It is believed that over 2.5 quintillion bytes of data are created every day, and this number is in increasing order. This has granted more users in all parts of the world, and thus the world has become a big dataset just waiting to be analyzed.
The data produced can be tapped into and then can be used for the betterment of products, services, marketing and can influence any field. Analysis of such data sets can find new correlations to spot business trends, prevent diseases, combat crimes and so on.
It has revolutionized fields such as science, financial technology, healthcare analytics, geographic information systems, urban informatics, business informatics, meteorology, genomics, and environmental research.
Today in this article lets take a deep look into some Big data projects.
So what are Big Data Projects?
Big Data projects are projects that focus on analysing, extracting information from data sets that are seemingly ”too large” or complex to be dealt with by the “traditional” data-processing softwares. Big Data projects often deal with predictive analysis, user behaviour analytics or other analytics methods that extract value from “big” data.
Relational database management systems(RDBMS) and desktop statistical software packages used to visualize data often have difficulty processing and analysing big data.
Thus we can understand that “the processing and analysis of big data may require massively parallel software running on many servers.“
Big data Projects in github:
GitHub is a code hosting platform for version control and collaboration. It lets you and others work together on projects from anywhere.
These are some of the most interesting open-source big data projects available as Github repos. The projects that are discussed below are open source, meaning anyone could download the code and run it on their machine at home, provided they have sufficient machine capabilities to cope up with the computational demand of big data projects.
The Apache NiFi helps in automating the data stream between various softwares.
The project applies predefined rules to streamline data flow. The contributors -matrix BI Limited, have made unique rules in JAVA, and thus it is a very handy big data project.
The big data project aims to draw useful insights about users and movies by leveraging different forms of Spark APIs. The semi-structured MovieLens dataset contains a million records and solves analytical questions using Spark and Scala.
The pandas profiling project aims to create HTML profiling reports. It also extends the pandas DataFrame objects, as the primary function isn’t suited for in-depth data analysis. The generated report is in HTML. It supports Boolean, numerical, Date, Categorical, File, Image types of abstraction and many more.
The big data project focuses on developing several simple maps to analyse one provided datasets. 18 million twitter messages during the 2012 London Olympics, which were related to the events happening in London, were captured and analysed. Major reports including hashtag analysis, the time analysis and the text analysis are filed based on the dataset.
Big Data Projects for students:
Let’s look at some of the Big data projects for beginners and students.
This is an excellent deep learning project for students and beginners. Text mining is in high demand and it will help you to showcase your talents as a data scientist. You’ll have to perform text analysis and visualisation of the documents provided. To begin, try this project on Kaggle.
Analysing patterns in crime that takes place in an area, can help law enforcement agencies to prevent future crimes, by identifying crime-infested areas and taking precautions. To begin with, try finding patterns, and validate your model using the data set provided on Kaggle.
The main aim of the big data project is to combat real-world cybersecurity problems by exploring vulnerability disclosure trends with complex multivariate data. This cybersecurity project seeks to establish an innovative and robust statistical framework to help the user gain an in-depth understanding of the dynamics and their dependence structures.
Hadoop is a software library designed by the Apache Foundation to enable distributed storage and processing of massive volumes of computation and datasets. This open-source service supports local computing and storage can deal with faults or failure at the application layer itself.
Why Hadoop is used for big data projects?
Hadoop offers a wide range of solutions and standard utilities that deliver throughput analysis, cluster resource management, and parallel processing of datasets. Important point to note is that even the tech giants such as Amazon Web Services, IBM Research, Microsoft deploy Hadoop for their operations.
Some important modules supported by Hadoop are:
Hadoop Distributed File System or HDFS
The Hadoop ecosystem has a very desirable ability to blend with popular programming and scripting platforms such as SQL, Java, Python etc.
Hadoop Big Data Projects
Relational database Management Systems(RDBMSs) were inefficient and failed to manage and pipeline the growing demand for current data. The failure of the RDBMSs triggered the transition to Hadoop.
Data migration from legacy systems to the cloud is a major use case in organizations. Being open-source Apache Hadoop and Apache Spark has been the popular choice to replace the old, legacy software tools which required maintenance and other costs.
Link Prediction is a recognized project in the big data field. Its application has an impact on many domains – especially social media. Given a graphical relationship between variables, an algorithm needs to be developed that can predict which two nodes are likely to be connected.
It can also make the financial industry smaller, where it is necessary to develop an algorithm that can be suggested for age, gender, location, education, page selection, friendly users
Trend Analysis on weblogs
You can design a log analysis system which can handle gigantic quantities of log files dependably. Such a program would minimize the response time for queries. It would work by analysing the activity trends based on the data from the browsing sessions, most visited web pages, trending keywords and so on.
Select any field, and there is actually a huge demand in specialised analysis that addresses the unique needs of the sector. Let it be social media, logistics, banking and finance, science and research, etc. Let’s take up banking and finances,there you can apply Hadoop in:
Distributed storage for risk mitigation or regulatory compliance
Time series analysis
Liquidity risk calculations
Monte Carlo Simulations
Big Data Projects In Infosys:
Walking the fine line between claims and Fraud:
Healthcare insurances often walk a fine line between underpaying the client and overpaying due to the usage of frauds from the client-side. An insurer, an Infosys client, according to the data, ended up overpaying 1 billion $ annually.
They decided to leverage advanced analytics techniques to remedy the situation. Specifically, they wanted to optimize the claims processing and payment systems by identifying potential overpayments, thereby identifying fraudulent claims and providers early in the cycle.
The Infosys team built a pipeline to ingest the stream of prepayment claims data into a Hadoop platform in real time. The system processed 50000 claims every 15 minutes. Each claim would be given a score, which if above a threshold value, was considered potential overpayments.
They also built a dashboard over this framework so that the executives could see, in real time, the claims being processed and how many were tagged for intervention and the reason.
With the new system, they were able to identify US$ 11 million in claims overpayment and prevent losses, leading to a net savings of US$ 5 million in the first year itself.
Infosys Genome Solution:
The Infosys team created a customer DNA gene factory model for a European sports
and lifestyle goods retailer with a global presence. The components of prefabricated genes utilized customer demographics, sales transactions, marketing campaigns, reviews, to understand customer behavior.
Approximately 6,000 customer attributes / genes were created for exploratory / predictive models. The solution:
• Inverted the time spent on data preparation and analytical modeling in numerous descriptive/predictive model-building exercises
• Covered almost 80 percent of all their business use-cases across marketing, sales, and operations
• Identified the right targets and enabled relevant communication / offers that were delivered on time
The big data project accelerated data-acquisition in a boundary less fashion, shortened time-to-market, delivered timely and contextual insights and also lowered cost of ownership.
Why You Should Get Into Big Data Projects And Analysis?
Big data analytics helps organizations harness their data and use it to identify new opportunities. These companies have ample information about the products and services, buyers and suppliers, consumer preferences that can be captured and analyzed.
The data, if used wisely, can result in cost savings, time reductions, and also as a driver of innovation and development of products. Many experts believe that Big Data Analytics still has untapped potentials that can Fastrack development in every major sector.
Every company may need to allocate some of their resources in the big data field to stay in the competition in the near future.
To get more ideas on big data projects, you can watch this video.
In this article, we have covered the whats and whys of Big Data Projects and have seen some of the most interesting big data projects to kickstart your career in data analytics. We have also seen some real-life applications and the problems tackled using data analytics by professionals (here we have taken big data projects from Infosys).
Hadoop is a common name in the field of big data analytics and, if mastered, is a skill that will go long in your data science career. Remember the future of big data, data science, and machine learning is getting brighter day by day.