Disadvantages of Big Data Technology

Collection of structured data for analysis and processing.
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maksudasm
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Joined: Thu Jan 02, 2025 7:11 am

Disadvantages of Big Data Technology

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There are also disadvantages to using big data:

Difficulties with scaling . For example, an online service is visited by a stable number of users during normal times. After an advertising campaign, the number of visitors suddenly increases many times over. Not every platform will remain operational with such an influx. The problem is solved by using cloud storage that can be quickly scaled.

High level of risks and threats . Big Data requires an increased level of security when working with it. This is especially critical in medicine, banking and some industries related to hazardous and harmful substances. The problem is partly solved by organizing distributed controlled access to databases. End-to-end encryption, authentication and some other effective measures are actively used.

High costs . Working with big data requires significant computing power, servers and other expensive equipment. It is only available to people who have undergone certain training and acquired specific skills.

Disadvantages of Big Data

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Problems of Big Data technology
Main difficulties:

A large array of information is often a advertising data package collection of disparate, unstructured data. Therefore, their analysis is difficult. The more criteria are used for selection, the higher the probability of obtaining an error.

Additional security measures are required to store and process data.

Manipulation of information containing users' personal data may lead to privacy issues.

It is possible that Big Data may be used in a not entirely clean manner. For example, when trying to influence elections or make money on the stock exchange using insider information.


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Frequently Asked Questions about Big Data Technology
Below we answer frequently asked questions about Big Data technology.

Where to start learning technology?
It is advisable to first study the theoretical foundations of working with information. Then you need to master the methodology of collecting and analyzing data. At this stage, it is not important whether the table will be for a hundred rows or for several million. The main thing is to comprehend the principle. Understanding the specifics of filters, cleaning and analyzing data will lay an excellent foundation for mastering any specialization when working with Big Data.

Is there a difference between Big Data and Data Science?
Data Science is a scientific discipline that studies the patterns of behavior of information flows, not necessarily huge ones. Mathematical and statistical research methods are used as tools.

Big Data is a gigantic database, often without a strictly ordered structure. The idea is to extract a rational kernel from a multitude of disparate and poorly related information that can be used in practical activities.

Are there any courses on how to work with information?
A profession in this field can be acquired by studying at some technical higher educational institutions in Russia, starting in 2023. There are also specialized online courses. Self-study requires a lot of time and effort, and is associated with a number of difficulties.
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