The week's pick
Random Articles
Reseach Article
Serverless Architecture for Real-Time Stock Market Data Analytics in Cloud Environments
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 75 |
| Year of Publication: 2025 |
| Authors: Gautam Solaimalai |
10.5120/ijca2025924625
|
Gautam Solaimalai . Serverless Architecture for Real-Time Stock Market Data Analytics in Cloud Environments. International Journal of Computer Applications. 186, 75 ( Mar 2025), 9-15. DOI=10.5120/ijca2025924625
Abstract
The methodology established in this paper presents AWS Lambda and DynamoDB to deliver continuous statistics on stock market data processing and the related analytics. This method is particularly economical, dynamic, and low latency due in part to the design's ability to include additional consumers to "scale up" or "scale down" depending on the incoming high-frequency trade supports. AWS Lambda functions can read and analyze box data that are constructed in a DynamoDB data table by processing incoming real-time data with calling financial APIs functions first. When running through predictive analytical models, some potential results could include LSTM (Long Short-Term Memory) and other ML models. While we outline improvements over existing systems, all of our results show a marginal impact on improvements demonstrated through improvements in our overall delay of 3% in the end-to-end processing time, 1% in pre-processing the input data across AWS Lambda, and 1% in processing time in total. We achieved a latency of around 150 ms and can handle 1000 requests concurrently, with resulting efficiency in scaling 95%. Other, significant features include fault recovery time in about 300 ms and a failure rate of 0.5%, which are indications of the fault tolerance of this design. This format brings a scalable and effective solution to financial market analytics, as it effectively employs a distributed microservices design into modern financial infrastructure, which are key to the high-volume processing involved in stock market data analytics.
References
- S. Poojara, C. K. Dehury, P. Jakovits, and S. N. Srirama, “Serverless Data Pipelines for IoT Data Analytics: A cloud Vendors Perspective and Solutions,” in Springer eBooks, 2022, pp. 107–132. doi: 10.1007/978-3-031-18034-7_7.
- V. S. Sakila and S. Manohar, “Real-time air quality monitoring in Bull Trench Kiln-based Brick industry by calibrating sensor readings and utilizing the Serverless Computing,” Expert Systems with Applications, vol. 237, p. 121397, Sep. 2023, doi: 10.1016/j.eswa.2023.121397.
- B. Singh, R. Martyr, T. Medland, J. Astin, G. Hunter, and J.-C. Nebel, “Cloud based evaluation of databases for stock market data,” Journal of Cloud Computing Advances Systems and Applications, vol. 11, no. 1, Sep. 2022, doi: 10.1186/s13677-022-00323-4.
- Z. Cai, Z. Chen, X. Chen, R. Ma, H. Guan, and R. Buyya, “SPSC: Stream Processing Framework atop serverless computing for industrial big data,” IEEE Transactions on Cybernetics, vol. 54, no. 11, pp. 6509–6517, Jun. 2024, doi: 10.1109/tcyb.2024.3407886.
- W. Chen, Z. Milosevic, F. A. Rabhi, and A. Berry, “Real-Time Analytics: concepts, architectures, and ML/AI considerations,” IEEE Access, vol. 11, pp. 71634–71657, Jan. 2023, doi: 10.1109/access.2023.3295694.
- D. Mileski and M. Gusev, “FinOps in Cloud-Native Near Real-Time Serverless Streaming Solutions,” 2023 31st Telecommunications Forum (TELFOR), pp. 1–4, Nov. 2023, doi: 10.1109/telfor59449.2023.10372626.
- P. K. Sekar, “THE DATA-DRIVEN FUTURE OF FINANCE: ADVANCES IN ENGINEERING FOR REAL-TIME ANALYTICS AND DECISION MAKING,” Oct. 09, 2024. https://ijrcait.com/index.php/home/article/view/IJRCAIT_07_02_006
- L. Calderoni, D. Maio, and L. Tullini, “Benchmarking cloud providers on serverless IoT Back-End infrastructures,” IEEE Internet of Things Journal, vol. 9, no. 16, pp. 15255–15269, Jan. 2022, doi: 10.1109/jiot.2022.3147860.
- V. Shahane, “Serverless computing in cloud environments: architectural patterns, performance optimization strategies, and deployment best practices,” Mar. 28, 2022. https://scienceacadpress.com/index.php/jaasd/article/view/18
- S. Albahli, A. Irtaza, T. Nazir, A. Mehmood, A. Alkhalifah, and W. Albattah, “A machine learning method for prediction of stock market using Real-Time Twitter data,” Electronics, vol. 11, no. 20, p. 3414, Oct. 2022, doi: 10.3390/electronics11203414.
- F. Correia, A. M. Madureira, and J. Bernardino, “Deep neural networks applied to stock market sentiment analysis,” Sensors, vol. 22, no. 12, p. 4409, Jun. 2022, doi: 10.3390/s22124409.
- K. Peddireddy, “Streamlining Enterprise Data Processing, Reporting and Realtime Alerting using Apache Kafka,” 2023 11th International Symposium on Digital Forensics and Security (ISDFS), pp. 1–4, May 2023, doi: 10.1109/isdfs58141.2023.10131800.
- F. G. D. C. Ferreira, A. H. Gandomi, and R. T. N. Cardoso, “Artificial Intelligence Applied to Stock Market Trading: A review,” IEEE Access, vol. 9, pp. 30898–30917, Jan. 2021, doi: 10.1109/access.2021.3058133.
- A. Oksanen, E. Mantere, I. Vuorinen, and I. Savolainen, “Gambling and online trading: emerging risks of real-time stock and cryptocurrency trading platforms,” Public Health, vol. 205, pp. 72–78, Mar. 2022, doi: 10.1016/j.puhe.2022.01.027.
- G. Kumar, S. Jain, and U. P. Singh, “Stock Market Forecasting Using Computational Intelligence: A survey,” Archives of Computational Methods in Engineering, vol. 28, no. 3, pp. 1069–1101, Feb. 2020, doi: 10.1007/s11831-020-09413-5.
- M. Ananthi and K. Vijayakumar, “RETRACTED ARTICLE: Stock market analysis using candlestick regression and market trend prediction (CKRM),” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 5, pp. 4819–4826, Apr. 2020, doi: 10.1007/s12652-020-01892-5.
Index Terms
Keywords