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Some Big Data Applications

  • July 01, 2023
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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Some Big Data Applications

Due to advancements in the data science sector, the significance of data science has greatly expanded over time. The most popular and rapidly developing field in the modern period is data science. Data science need has grown significantly over time along with the rise in data consumption. However, all physical labour is being replaced by the current digital trend. As a result, the amount and volume of data are growing daily. Storage problems develop as a result of the data's growing amount and quantity. Large amounts of data become more challenging to store on basic computers. You must learn about various storage solutions if you want to store big amounts of data. Big data technology was developed to address the problems associated with storing very large amounts of data.

Terabytes of data may be stored using large-scale data technologies like Hadoop and Spark. Big data technologies have resolved the storage and application of the proper analytics on the particular data set challenges. Different big data uses and applications have been addressed by our team of specialists. The instances that are given here are a few.

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  • Recommender Systems:

    Probability sampling is also sometimes called random sampling, is used a lot in data science and machine learning. It is the most used type of sampling in data science and machine learning. In this sampling, the chances of every element to be selected in the specific sample are always equal. In this sampling, the data scientists select the required data elements randomly from the total population of data elements. Random sampling sometimes can give you high accuracy after feeding the data set, and in some cases, there can be a very low performance of the data science model, which uses random sampling. So, random sampling should always be done very carefully so that the selected data records represent the whole data set. 

    In real life, the recommender system is used to several disciplines. Systems for eCommerce employ the recommender system. The eCommerce platforms can suggest various goods to site visitors and product purchasers. The recommender system may offer clients suggestions for many kinds of related items when they purchase a product. The recommender systems function based on the various purchasing histories of the customers as well as the customers' interest in additional items. The system makes predictions about new items for the consumer using machine learning models. Big data technology may be quite useful in creating recommender systems that are more accurate. The accuracy of the system improves when we train machine learning models with additional data. Big data technologies also make it possible to use the most current and up-to-date data throughout the model-building process' training phase.

    The recommender systems are also used on different websites. The blogging websites also use recommender systems that help the website visitors to read the related articles or blogs on that website. Moreover, the blog websites can store the data of the visitors to find out the interest of the visitors and then next time recommend the articles or blogs related to his or her interest. These recommender systems work using the machine learning algorithms and association rule mining algorithms of data mining.

    Another excellent illustration in this context is the mechanism for recommending courses to students. To determine the students' interests in a given topic, this method analyses prior subject data and student performance in those subjects. These systems choose the courses in accordance with their historical data using various machine learning and data science methods. Big data and data science may both assist students in selecting the courses in which they will perform well. By looking into the history of a student's performance in the pertinent disciplines, the student recommender systems can also assist students in forecasting grades.

    The recommender systems can also be used in the medical field that can recommend the medicines to the specific patient by analyzing the different diseases record of that specific patient. This system can then recommend medicine for that patient based on previous medical history. Big data technologies help a lot in building different types of recommended systems by choosing large data sets for the training. Moreover, the most recent data sets can be used for the training phase of the data science model building.

    The system becomes more accurate as a consequence. The data science algorithms will work more properly when we utilise the most recent and updated data sets for training than when we use the older data sets. Modern data is used by big data technology, which may store data in terabytes and beyond. These data science models may be built more accurately and quickly with the use of the spark and Hadoop technologies.

  • Digital Advertisements

    Data science technologies provide the latest methods for advertisement purposes. The digital ad systems also use big data and machine learning technologies to help the website visitors click the ads according to their interest and desire. The ads providing companies and browsers collect the history if the ads click from a specific website visitor and then train the model of data science or machine learning and provide different ads for the website visitors according to their previous history of click the ads. Similar and most related ads are recommended by using big data technologies and data science methods.

    So, in order to suggest various adverts to clients and website visitors, digital ad agencies also utilise cutting-edge technologies like Spark and Hadoop. This increases the corporation, business, or brand's income and profit via advertising and marketing. Big data technologies make it possible to create data science models for this purpose utilising the most recent and freshest data.

    When the latest data set is used using big data technologies, the accuracy and performance of the data science model increase abruptly. As a result, the marketing companies get good results and can generate successful campaigns.

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  • Search Engine Queries

    The tools for big data and data science assist browsers in enhancing their query recommender systems. In this method, the data scientist makes advantage of the past search history of various online searchers and applies these keywords to forecast the subsequent word of that query. The precise query submitted by the web searchers is automatically completed by the browsers. The most recent data science and machine learning models, including several kinds of artificial neural networks and their most recent algorithms, are utilised for this system. These systems' accuracy is increased by utilising the most recent data sets, big data technology, and data science models.

    The applications of the major big data technologies have been thoroughly addressed by our team of specialists. Visit our website often to see more articles on data science.

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