Scalable Data Processing Architectures with Mongodb, Postgresql, and Kafka

  • Authors

    • Venkatesh Satla Full Stack Lead Java Developer at Innosoul, Inc, USA. Author
    • Ramesh Jindam Senior Technical Lead at Wells Fargo, USA. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2024PI3M8G

    Published 05-05-2024

  • Scalable Data Processing, Mongodb, Postgresql, Apache Kafka, Data Pipelines, Distributed Systems, Real-Time Analytics, Event Streaming, Big Data Architecture, Microservices

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

  • Abstract

    As businesses increase their reliance on data-driven decision-making, the need for extensive and fault-tolerant data processing systems is becoming a vital issue like never before. The data generated by modern applications is enormous and rapid, furthermore, it comes from a variety of sources such as web platforms, mobile devices, IoT sensors, and enterprise systems. This situation creates major problems not only in storage but also in processing and in real-time analytics. Actually, the previous single-unit databases are often incapable of efficiently handling the very large size, speed, and diversity of today's data loads. Therefore, to solve these issues, companies are switching to distributed architectures that enable the combination of different specialized technologies for data ingestion, storage, and processing. This paper presents a scalable data processing architecture developed with the help of Apache Kafka, MongoDB, and PostgreSQL, each platform's strengths being utilized to form a strong and flexible data environment. Kafka is used as the event streaming layer with a very high-throughput, allowing in a reliable way the real-time data ingestion as well as the interaction between different services. MongoDB is a NoSQL database that gives management of quite different data sets that are also changing very fast, as it is very flexible with the schema, whereas PostgreSQL provides a very high level of transactional consistency and also sophisticated query capabilities of the relational type for highly organized business data. The suggested architecture combines these technologies so that it can serve both requirements of real-time and batch processing while at the same time remaining scalable, fault-tolerant, and operational efficient. Presenting a real-life example is done here for showing how the architecture deals with very large data streams, assists analytical workloads, and guarantees smooth data flow throughout the different components of the system.

  • References

    [1] Muller, J., & Fischer, L. (2020). Scalable Data Architectures for Real-Time Big Data Analytics: A Comparative Study of Hadoop, Spark, and Kafka. International Journal of AI, BigData, Computational and Management Studies, 1(4), 8–18. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I4P102

    [2] Muppaneni, R. K. (2020). Retail Reimagined: How Dynamics 365 Commerce Is Driving Omnichannel Experiences. International Journal of AI, BigData, Computational and Management Studies, 1(1), 49–59. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I1P106

    [3] Parakala, A. (2023). Vendor Highlights—IoT, AI, and Process Mining. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 135–146. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P115

    [4] Kodakandla, P. (2023). Real-Time Data Pipeline Modernization: A Comparative Study of Latency, Scalability, and Cost Trade-Offs in Kafka-Spark-BigQuery Architectures. International Research Journal of Modernization in Engineering Technology and Science, 5(10), 3340–3349. https://doi.org/10.56726/IRJMETS45355

    [5] Srigadde, B. R. (2023). The Hidden Gem: Lightning Headless Component. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 244–254. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P125

    [6] Banerjee, Sinchan. "Scalable Data Architecture with Java." Build Efficient Enterprise-Grade Data Architecting Solutions Using Java/Sinchan Banerjee (2022).

    [7] Takkalapally, D., & Takkellapally, M. R. (2023). GC-TuneHFT: AI-Based Garbage Collection Optimization in High-Frequency Trading Environments. American International Journal of Computer Science and Technology, 5(6), 25–37. https://doi.org/10.63282/3117-5481/AIJCST-V5I6P103

    [8] Kumar Doodala, A. N., Thatraju, S., & Kankanala, V. (2023). Post-Pandemic QA Evolution in Healthcare IT. International Journal of Emerging Trends in Computer Science and Information Technology, 4(2), 223–232. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P122

    [9] Ortiz, Isabella. "Integrating advanced data handling approaches in modern architectural designs to optimize efficiency and scalability." Journal of Sustainable Technologies and Infrastructure Planning 7.1 (2023): 97-134.

    [10] Shiramalla, R. (2023). Optimizing Cross-Platform Enterprise Integrations Using Workato: A Case Study of Salesforce and Oracle SaaS Applications. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 232–243. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P124

    [11] Kommuru, Madhurima, and Appala Nooka Kumar Doodala. "Performance Bottlenecks Necks in Data Heavy Python Applications." International Journal of Applied Data Science & Modern Computing 6.2 (2023): 01-19.

    [12] Muppaneni, K. (2023). Virtual DOM vs Real DOM: Performance Benchmarks. International Journal of AI, BigData, Computational and Management Studies, 4(4), 180–189. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P118

    [13] Thota, Madhava Rao. "From data centers to cloud platforms: A scalable framework for database and big data migration." Journal of Scientific and Engineering Research (2017).

    [14] Vppalapati, M., & Talasila, P. K. . (2023). Unobservable Performance: Storage Failures That Leave No Metrics Behind. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 177-188. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P120

    [15] Shen, Liqun, et al. "Meteorological sensor data storage mechanism based on timescaledb and kafka." International Conference of Pioneering Computer Scientists, Engineers and Educators. Singapore: Springer Singapore, 2019.

    [16] Suryadevara, S. S. K. (2021). AI-Driven Multi-Cloud Orchestration System for Enterprise Digital Experience Delivery. American International Journal of Computer Science and Technology, 3(1), 21–34. https://doi.org/10.63282/3117-5481/AIJCST-V3I1P103

    [17] Gaddam, R. R., & Krishna, K. (2023). KFP v2 Artifact-Centric ML Pipeline Governance. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(2), 142–153. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I2P116

    [18] Akanbi, Adeyinka, and Muthoni Masinde. "A distributed stream processing middleware framework for real-time analysis of heterogeneous data on big data platform: Case of environmental monitoring." Sensors 20.11 (2020): 3166.

    [19] Muppaneni, R. K. (2023). Low-Code Revolution: How Power Platform Extends Dynamics 365 Capabilities. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(3), 162–171. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I3P119

    [20] Allenki, S. S. (2023). Applying Cloud Security Best Practices in Regulated Environments. American International Journal of Computer Science and Technology, 5(3), 48–60. https://doi.org/10.63282/3117-5481/AIJCST-V5I3P105

    [21] Vanapalli, Satyendra Kumar. "Data-Driven Decision Making: Integrating AI Tools with CRM Platforms." International Journal of Applied Data Science & Modern Computing 6.1 (2023): 01-21.

    [22] Chowdhury, Arnab Ghosh, et al. "An approach for data pipeline with distributed query engine for industrial applications." 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). Vol. 1. IEEE, 2020.

    [23] Parakala, A. (2023). Citizen-Facing Automation: Chatbots and Self-Service in Public Services. International Journal of AI, BigData, Computational and Management Studies, 4(4), 108–118. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P112

    [24] Emma, O., and P. Peace. "Building an Automated Data Ingestion System: Leveraging Kafka Connect for Predictive Analytics." Researchgate. net (2023).

    [25] Suryadevara, S. S. K., & Nakirikanti, S. (2023). Privacy-Preserving Personalization Using Federated Learning in AEM. International Journal of AI, BigData, Computational and Management Studies, 4(4), 190–199. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P119

    [26] Waizenegger, Tim, and Thomas Lumpp. "IBM cloud databases: Turning open source databases into cloud services." BTW 2019. Gesellschaft für Informatik, Bonn, 2019.

    [27] Allenki, S. S. (2023). Reducing Security Vulnerabilities With Encryption, IAM, and Regular Audits. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 265–275. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P127

    [28] Menon, Pradeep. Data Lakehouse in Action: Architecting a modern and scalable data analytics platform. Packt Publishing Ltd, 2022.

    [29] Vppalapati, M. (2023). When Identity Decisions Throttle Data Movement. International Journal of Emerging Research in Engineering and Technology, 4(3), 160–170. https://doi.org/10.63282/3050-922X.IJERET-V4I3P117

    [30] Gaddam, R. R. (2021). Vertex AI as a Unified Control Plane for MLOps. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(2), 92–102. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I2P110

    [31] Hamami, F., and I. A. Dahlan. "The implementation of stream architecture for handling big data velocity in social media." Journal of Physics: Conference Series. Vol. 1641. No. 1. IOP Publishing, 2020.

    [32] Vanapalli, S. K. (2023). Reducing Financial Fraud Using Machine Learning and CRM Data Models. International Journal of Machine Learning and Predictive Analytics, 6(2), 1–20.

    [33] Takkalapally, D., & Takkellapally, M. R. (2023). GC-TuneHFT: AI-Based Garbage Collection Optimization in High-Frequency Trading Environments. American International Journal of Computer Science and Technology, 5(6), 25–37. https://doi.org/10.63282/3117-5481/AIJCST-V5I6P103

    [34] Weißbach, Manuel, Hannes Hilbert, and Thomas Springer. "Performance Analysis of Continuous Binary Data Processing using Distributed Databases within Stream Processing Environments." CLOSER. 2020.

    [35] Katangoori, S., & Katangoori, A. (2023). Intelligent ETL Orchestration With Reinforcement Learning and Bayesian Optimization. International Journal of Emerging Research in Engineering and Technology, 4(4), 208–219. https://doi.org/10.63282/3050-922X.IJERET-V4I4P123

    [36] Debauche, Olivier, et al. "Towards a unified architecture powering scalable learning models with IoT data streams, blockchain, and open data." Information 14.6 (2023): 345.

    [37] Kumar Doodala, A. N. (2023). Offline-First Android Architecture for Waste Management in Low Connectivity Zones. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 201–209. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P121

    [38] Srigadde, B. R. (2023). Creating Object Quick Actions With Lightning Web Components. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(2), 167–180. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I2P118

    [39] Muppaneni, K., & Vejella, M. (2023). Security and Data Privacy in Redux Stores. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 153–162. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P117

    [40] Duggirala, Siddhartha. "Newsql databases and scalable in-memory analytics." Advances in Computers. Vol. 109. Elsevier, 2018. 49-76.

    [41] Taluri, R. (2022). Cloud Data Engineering Strategies for Large-Scale Financial Data Integration and Intelligent Corporate Performance Reporting. International Journal of Emerging Research in Engineering and Technology, 3(4), 176-188. https://doi.org/10.63282/3050-922X.IJERET-V3I4P119

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