Authors

  • Manzura Maxdiyeva
    Asia International University
  • Tursunbek Jalolov
    Asia International University

DOI:

https://doi.org/10.71337/inlibrary.uz.jasss.121678

Abstract

This in the article digital transformation in the process big in size The concept of Big Data , its main characteristics , technological basis , practical application , advantages and face bride problems about in detail information Big Data is today's on the day economy , health conservation , education , industry , social networks such as in the fields important place This is​ article big information with worker professionals , students and IT industry with interested parties for important manual task will do .

 

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Volume 15 Issue 06, June 2025

Impact factor: 2019: 4.679 2020: 5.015 2021: 5.436, 2022: 5.242, 2023:

6.995, 2024 7.75

http://www.internationaljournal.co.in/index.php/jasass

805

BIG DATA AND ITS IMPORTANCE

Jalolov Tursunbek Sadriddinovich

Asia International University associate professor PhD

Maxdiyeva Manzura Muxiddinovna

Asia International University student

Annotation:

This in the article digital transformation in the process big in size The concept of

Big Data , its main characteristics , technological basis , practical application , advantages and

face bride problems about in detail information Big Data is today's on the day economy , health

conservation , education , industry , social networks such as in the fields important place This

is​ article big information with worker professionals , students and IT industry with interested

parties for important manual task will do .

Login

Information technologies sharp development , internet, mobile devices , social networks , IoT

(Internet of Things) and other digital sources through being created information size sharp is

increasing . Every day in activity humanity in front of many digital traces leaves : shopping ,

navigation , health about information , social activities and This information​

not only big in

size , maybe various in forms , high at speed and various in accuracy Such information will be

simple methods with analysis to do possible no , that's it because Big Data technologies to the

surface arrived .

Home part

1. What is Big Data ?

Big Data is size very large , structured and unstructured information They are complex . again

performance , analysis to do and from it useful information to take traditional information base

systems using difficult Big Data technologies such information on effective work opportunity

gives .

2. Big Data main Features : "5V model "

- Volume​

- Velocity​

- Variety (Various forms )

- Veracity​

- Value​

3. Big Data technologies and platforms

- Hadoop, Spark, MongoDB, Cassandra, AWS, Google Cloud, Power BI and others

4. Big Data architecture

-

Information collection , storage , recycling performance , analysis and visualization stages


background image

Volume 15 Issue 06, June 2025

Impact factor: 2019: 4.679 2020: 5.015 2021: 5.436, 2022: 5.242, 2023:

6.995, 2024 7.75

http://www.internationaljournal.co.in/index.php/jasass

806

5. Big Data Specializations

- Data Analyst, Data Scientist, Data Engineer, ML Engineer

6. Application sectors

- Health storage , banking, marketing, industry , agriculture agriculture , transport

7. Advantages

- Fast decision acceptance to do , automation , competitiveness

8. Problems

- Privacy , security , infrastructure expenses , specialist shortage

9. Big Data and AI integration

- AI's effective performance Big Data is needed for

10. Future prospects

- Quantum computing, DaaS, automated data engineering

Conclusion

Big Data technologies digital of the economy basis is , data over effective management provides .

Large- scale from data benefit to take not only companies , maybe whole society for new

opportunities creates them . right management , analysis to do and protection to do modern

technological of progress is the key .

Used literature

1. "Big Data Fundamentals" - Thomas Earle

2. Apache Hadoop & Spark official documents

3. IBM Big Data Analytics whitepaper

4. Microsoft Azure Big Data Documentation

5. Google BigQuery references

6. Coursera – Big Data Specialization

7. DataCamp – Data Science and Analytics courses

8. O'Reilly Media – Data Engineering Books

9. Stack Overflow and GitHub – open source projects

10. Kaggle – a real project and analyses

References

"Big Data Fundamentals" - Thomas Earle

Apache Hadoop & Spark official documents

IBM Big Data Analytics whitepaper

Microsoft Azure Big Data Documentation

Google BigQuery references

Coursera – Big Data Specialization

DataCamp – Data Science and Analytics courses

O'Reilly Media – Data Engineering Books

Stack Overflow and GitHub – open source projects

Kaggle – a real project and analyses