Designing Scalable Data Pipelines for Real-Time Analytics in Big Data Systems
DOI:
https://doi.org/10.63282/3050-922X.ICRCEDA25-131Keywords:
Scalable data pipelines, real-time analytics, big data, distributed computing, stream processing, Lambda architecture, Kappa architecture, cloud computing, fault tolerance, data ingestionAbstract
The exponential growth of data in the modern digital era necessitates efficient and scalable data processing mechanisms to extract meaningful insights in real time. Real-time analytics enables organizations to process, analyze, and visualize data streams instantaneously, providing critical insights that drive decision-making processes. However, designing scalable data pipelines for real-time analytics in big data systems presents several challenges, including data ingestion bottlenecks, efficient processing architectures, and ensuring low-latency responses. This paper explores the fundamental principles and methodologies involved in building scalable data pipelines, emphasizing architectural paradigms such as Lambda and Kappa architectures, and the role of distributed computing frameworks, stream processing engines, and cloud-based solutions. The paper further examines the impact of various data pipeline components, including data ingestion, processing, storage, and visualization, while discussing best practices for optimizing system performance, fault tolerance, and cost-effectiveness. A literature survey provides a comparative analysis of state-of-the-art real-time analytics frameworks and their scalability aspects. The methodology outlines the step-by-step design and implementation process of scalable data pipelines, supported by empirical evaluations. The results and discussions section presents performance benchmarks, evaluates latency metrics, and assesses the effectiveness of different data processing strategies. The paper concludes with recommendations for future research directions and potential improvements in scalable data pipeline design
References
[1] Hamari, J., Koivisto, J., & Sarsa, H. (2014). Does gamification work? — A literature review of empirical studies on gamification. In the Proceedings of the 47th Hawaii International Conference on System Sciences, Hawaii, USA, January 6–9, 2014. DOI:10.1109/HICSS.2014.377
[2] Panyaram, S., & Kotte, K. R. (2025). Leveraging AI and Data Analytics for Sustainable Robotic Process Automation (RPA) in Media: Driving Innovation in Green Field Business Process. In Driving Business Success Through Eco-Friendly Strategies (pp. 249-262). IGI Global Scientific Publishing.
[3] Bhagath Chandra Chowdari Marella, “Driving Business Success: Harnessing Data Normalization and Aggregation for Strategic Decision-Making”, International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING, vol. 10, no.2, pp. 308 – 317, 2022. https://ijisae.org/index.php/IJISAE/issue/view/87
[4] Zichermann, G., & Cunningham, C. (2011). Gamification by Design: Implementing Game Mechanics in Web and Mobile Apps. O’Reilly Media, Inc.
[5] Palakurti, A., & Kodi, D. (2025). “Building intelligent systems with Python: An AI and ML journey for social good”. In Advancing social equity through accessible green innovation (pp. 1–16). IGI Global.
[6] Maroju, P.K.; Bhattacharya, P. Understanding Emotional Intelligence: The Heart of Human-Centered Technology. In Humanizing Technology with Emotional Intelligence; IGI Global Scientific Publishing: Hershey, PA, USA, 2025; pp. 1–18.
[7] Werbach, K., & Hunter, D. (2012). For the Win: How Game Thinking Can Revolutionize Your Business. Wharton Digital Press.
[8] Lakshmi Narasimha Raju Mudunuri, Pronaya Bhattacharya, “Ethical Considerations Balancing Emotion and Autonomy in AI Systems,” in Humanizing Technology With Emotional Intelligence, IGI Global, USA, pp. 443-456, 2025.
[9] Aragani, V. M. (2023). “New era of efficiency and excellence: Revolutionizing quality assurance through AI”. ResearchGate, 4(4), 1–26.
[10] Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78(4), 772.
[11] Puvvada, Ravi Kiran. "Industry-Specific Applications of SAP S/4HANA Finance: A Comprehensive Review." International Journal of Information Technology and Management Information Systems(IJITMIS) 16.2 (2025): 770-782.
[12] Kirti Vasdev. (2019). “AI and Machine Learning in GIS for Predictive Spatial Analytics”. International Journal on Science and Technology, 10(1), 1–8. https://doi.org/10.5281/zenodo.14288363
[13] Bunchball. (2010). Gamification 101: An Introduction to the Use of Game Dynamics to Influence Behavior.
[14] Empowering the Future: The Rise of Electric Vehicle Charging Hubs - Sree Lakshmi Vineetha Bitragunta - IJLRP Volume 5, Issue 11, November 2024, PP-1-10, DOI 10.5281/zenodo.14945815.
[15] S. Panyaram, "Connected Cars, Connected Customers: The Role of AI and ML in Automotive Engagement," International Transactions in Artificial Intelligence, vol. 7, no. 7, pp. 1-15, 2023.
[16] Gartner. (2011). Gartner says by 2015, more than 50 percent of organizations that manage innovation processes will gamify those processes. Gartner Press Release.
[17] Padmaja Pulivarthy. (2024/12/3). Harnessing Serverless Computing for Agile Cloud Application Development,” FMDB Transactionson Sustainable Computing Systems. 2,( 4), 201-210, FMDB.
[18] Puvvada, R. K. (2025). Enterprise Revenue Analytics and Reporting in SAP S/4HANA Cloud. European Journal of Science, Innovation and Technology, 5(3), 25-40.
[19] InfoQ. (2021). Gamification: A Strategy for Enterprises to Enable Digital Product Practices. InfoQ Article.
[20] A Novel AI-Blockchain-Edge Framework for Fast and Secure Transient Stability Assessment in Smart Grids, Sree Lakshmi Vineetha Bitragunta, International Journal for Multidisciplinary Research (IJFMR), Volume 6, Issue 6, November-December 2024, PP-1-11.
[21] P. K. Maroju, "Enhancing White Label ATM Network Efficiency: A Data Science Approach to Route Optimization with AI," FMDB Transactions on Sustainable Computer Letters, vol. 2, no. 1, pp. 40-51, 2024.
[22] Venkata SK Settibathini. Data Privacy Compliance in SAP Finance: A GDPR (General Data Protection Regulation) Perspective. International Journal of Interdisciplinary Finance Insights, 2023/6, 2(2), https://injmr.com/index.php/ijifi/article/view/45/13
[23] ProductPlan. (Year unknown). Title unavailable. ProductPlan publication.
[24] Muniraju Hullurappa, Mohanarajesh Kommineni, “Integrating Blue-Green Infrastructure Into Urban Development: A Data-Driven Approach Using AI-Enhanced ETL Systems,” in Integrating Blue-Green Infrastructure Into Urban Development, IGI Global, USA, pp. 373-396, 2025.
[25] L. N. R. Mudunuri, V. M. Aragani, and P. K. Maroju, "Enhancing Cybersecurity in Banking: Best Practices and Solutions for Securing the Digital Supply Chain," Journal of Computational Analysis and Applications, vol. 33, no. 8, pp. 929-936, Sep. 2024.
[26] Tomé Klock, A. C., Santana, B. S., & Hamari, J. (2023). Ethical Challenges in Gamified Education Research and Development: An Umbrella Review and Potential Directions. Preprint on arXiv.
[27] Venu Madhav Aragani, Arunkumar Thirunagalingam, “Leveraging Advanced Analytics for Sustainable Success: The Green Data Revolution,” in Driving Business Success Through Eco-Friendly Strategies, IGI Global, USA, pp. 229- 248, 2025.
[28] Chib, S., Devarajan, H. R., Chundru, S., Pulivarthy, P., Isaac, R. A., & Oku, K. (2025, February). Standardized Post-Quantum Cryptography and Recent Developments in Quantum Computers. In 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT) (pp. 1018-1023). IEEE.
[29] “Negative Effects of Gamification in Education Software: Systematic Mapping and Practitioner Perceptions.” (2023). Preprint on arXiv.
[30] Sree Lakshmi Vineetha Bitragunta, 2022. "Field-Test Analysis and Comparative Evaluation of LTE and PLC Communication Technologies in the Context of Smart Grid", ESP Journal of Engineering & Technology Advancements 2(3): 154-161.
[31] A. K. K, G. C. Vegineni, C. Suresh, B. C. Chowdari Marella, S. Addanki and P. Chimwal, "Development of Multi Objective Approach for Validation of PID Controller for Buck Converter," 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), Bhimtal, Nainital, India, 2025, pp. 1186-1190, doi: 10.1109/CE2CT64011.2025.10939724.
[32] Puvvada, R. K. "SAP S/4HANA Cloud: Driving Digital Transformation Across Industries." International Research Journal of Modernization in Engineering Technology and Science 7.3 (2025): 5206-5217.
[33] Swathi Chundru, Lakshmi Narasimha Raju Mudunuri, “Developing Sustainable Data Retention Policies: A Machine Learning Approach to Intelligent Data Lifecycle Management,” in Driving Business Success Through EcoFriendly Strategies, IGI Global, USA, pp. 93-114, 2025.
[34] Patibandla, K. K., Daruvuri, R., & Mannem, P. (2025, April). Enhancing Online Retail Insights: K-Means Clustering and PCA for Customer Segmentation. In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT) (pp. 388-393). IEEE.
[35] Maroju, P. K. (2024). Advancing synergy of computing and artificial intelligence with innovations challenges and future prospects. FMDB Transactions on Sustainable Intelligent Networks, 1(1), 1-14.
[36] Kommineni, M., & Chundru, S. (2025). Sustainable Data Governance Implementing Energy-Efficient Data Lifecycle Management in Enterprise Systems. In Driving Business Success Through Eco-Friendly Strategies (pp. 397-418). IGI Global Scientific Publishing.
[37] Advanced Technique for Analysis of the Impact on Performance Impact on Low-Carbon Energy Systems by Plant Flexibility, Sree Lakshmi Vineetha Bitragunta1 , Lakshmi Sneha Bhuma2 , Gunnam Kushwanth3, International Journal for Multidisciplinary Research (IJFMR), Volume 2, Issue 6, November-December 2020,PP-1-9.
[38] Khan, S., Uddin, I., Noor, S. et al. “N6-methyladenine identification using deep learning and discriminative feature integration”. BMC Med Genomics 18, 58 (2025). https://doi.org/10.1186/s12920-025-02131-6.
[39] Vootkuri, C. (2025). Multi-Cloud Data Strategy & Security for Generative AI.
[40] Priscila, S. S., Celin Pappa, D., Banu, M. S., Soji, E. S., Christus, A. T., & Kumar, V. S. (2024). Technological Frontier on Hybrid Deep Learning Paradigm for Global Air Quality Intelligence. In P. Paramasivan, S. Rajest, K. Chinnusamy, R. Regin, & F. John Joseph (Eds.), Cross-Industry AI Applications (pp. 144-162). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-5951-8.ch010
