Introduction

Modern-day life is driven by huge developments over the last five years in Big Data projects, machine learning- ML and Big Data analytics. The many issues of development and completely utilising the possibilities of both these fields are still being explored. One needs the Hadoop Ecosystem, knowledge of the various frameworks and some prior experience in the implementation of big data projects to be successful in Big Data Projects. Additionally, knowledge of the Hadoop MapReduce framework provides distributed computing scalability of Big Data analytics projects and better processing power. 

To attain such huge processing power in Big Data projects, some of the ways are

  • Cloud Computing: Moving data storage to the clouds has improved computing power for users who can use the services of data centres offered by companies like Google, Amazon, Microsoft etc., for specific time periods and as per individual user needs. This means that advanced computing power is available to users at negligible costs and infrastructure.
  • Setting up Parallel Computing Distributed Cluster Servers: One can also set up a server for storage and processing of data using a multiple core system with high memory and computing power. Big data needs a huge memory and storage capacity. Besides, multiple threaded Big Data Hadoop projects need different cores for their processing. This is a more expensive and captive use of Big Data mini-projects. One could also use a parallel combination of multiple smaller machines with a well-distributed workload to achieve the same result in Big Data analysis projects. This method also provides for better scalability. Note that such computing power is way more expensive than using cloud computing for Big Data projects for beginners.

List of projects

2021 is bound to see a lot of action in Big Data projects, and the five Big Data projects ideas mentioned below are both exciting and innovative.

  • Fraud Detection: Modern Big Data projects means hackers are ever prepared for technological exploitation of text messages, emails, financial transactions, oral communications and more. Fraud detection in such various data sources is a daunting task requiring much more than human intelligence alone. These applications present huge demand across domains and use and are worth watching out for.
  • Crime Prediction: Like fraud detection, the field of Big Data projects in crime prediction is also dependent on machine learning evolution to predict and detect such crimes. In the process of ML, historical crime data, subjects, crime locations, descriptions of perpetrators and subjects, occurrence time, etc., are used to train the frameworks of the model’s machine learning capabilities. These applications have a wide variety of data points, and the traffic of data of just a day in metropolitan areas sufficient to overload storage capacities of average computer systems. Thus storage optimised and efficient models are the need of the hour and are required not just in testing but also in the faster processing of data. Hence it is interesting to see how this field develops.
  • Traffic prediction and simulation: The Big Data projects simulation and prediction of traffic in real-time have many uses and benefits. The field of simulating traffic in real-time has been successfully modelled. But predicting route traffic continues to be a long-standing problem. This is so as predictive models for real-time traffic prediction is a highly complex task involving much latency, vast volumes of data and ever-rising costs. The models developed will need to be supremely efficient in real-time analysis with scalable infrastructure. SuMO and Open Traffic are two such models that are worth watching and waiting for.
  • Nuclear Physics Data Analysis: This modern world may appear simple, but in reality, it is hugely complex from the standpoint of Big Data projects and data analysis. Organisations like CERN regularly release large amounts of their data for analysis and research by the general public users. This data is Big Data and captures ten different dimensions, each having more significant than a billion data points. This means that when data reaches a trillion with 12 zeroes of data points, the frameworks would need to scale accordingly. The field of nuclear physics and such super scaling frameworks need them to be super scalable and use over 100,000 nodes when working or analysing such huge data volumes. This field sees much improvement and research in universities for achieving timely computations, which will be interesting to study.
  • Natural Language Modeling: The computer languages of modern-day Big Data projects and technologies are already integrated with NLP or Natural Language Processors. This type of computing makes the computer language hard to understand since it is not simple and is ‘context-free’ when used, unlike human languages, which are context-rich and easily understood. Big Data is a complex database that requires an extensive knowledge database, context, grammar etc. The use of NLP and AI (Artificial Intelligence) is hence bound to increase with Siri, Alexa etc., leading the way. Bridging the gaps in NLP modelling will make the models closer to humans and a better experience for users. This field of modelling the NLPs will be fascinating to watch out for.

Conclusion

Big Data projects are being generated day in and day out, and valuable applications being developed by the hour. Yet, we are a far way from actually harnessing Big Data. The above verticals and Big Data example projects discussed have massive potential for advancement and resolution of glitches faced in developing areas which 2021 will draw focus to. 

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

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