Auto-Scaling Data Lake Architectures for Event-Driven Analytics

  • Authors

    • Ethan Harris Senior Software Engineer, Google, USA. Author
    • Dr. Chen Wei Professor, Peking University, China. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2019PI3D9R

    Published 04-10-2019

  • Data Lake, Auto-scaling, Event-Driven Analytics, Real-time Data Processing, Cloud Computing, Scalability, Elastic Computing, Event Streaming, Apache Kafka, Serverless Computing, Cloud Architectures, Resource Management

    Issue

    Vol. 2 No. 1 (2019)

    Section

    Articles

    How to Cite

    [1]
    E. Harris and C. Wei, “Auto-Scaling Data Lake Architectures for Event-Driven Analytics”, IJDEIC, vol. 2, no. 1, pp. 01–10, Apr. 2019, doi: 10.67228/30715717/IJDEIC-2019PI3D9R.

  • Abstract

    In today’s data-driven world, data lakes have emerged as a crucial architectural pattern for storing large volumes of structured and unstructured data. However, the integration of event-driven analytics into data lake architectures presents unique challenges, especially in terms of scalability, latency, and resource management. This paper explores the concept of auto-scaling within data lake environments, specifically for event-driven analytics workloads. We delve into the fundamental challenges of scaling data lakes to accommodate high-throughput, real-time event streams while maintaining optimal performance. The paper outlines various auto-scaling techniques, including cloud-native solutions, container orchestration, and serverless computing, as effective mechanisms for ensuring dynamic scaling based on demand. We further examine the benefits and limitations of these solutions through industry case studies, offering insights into best practices and real-world implementation strategies. By providing a comprehensive overview of auto-scaling techniques and their application to event-driven analytics, this paper aims to offer valuable guidelines for organizations looking to enhance their data lake architecture for real-time decision-making.

  • References

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    [4] "AWS Auto Scaling." AWS Documentation, Amazon Web Services.

    [5] "Azure Autoscale Documentation," Microsoft Azure.

    [6] Fang, H. (2015). Managing Data Lakes in Big Data Era: What's a Data Lake and Why Has It Become Popular in Data Management Ecosystem. Proceedings of the IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, pp. 820–824. Discusses data lake concepts, architecture, and big data management challenges.

    [7] Chessell, M., Jones, N. L., Limburn, J., Radley, D., & Shank, K. (2015). Designing and Operating a Data Reservoir. IBM Redbooks. Presents architectural principles for scalable data reservoirs and enterprise data lake deployments.

    [8] Thusoo, A., & Sharma, B. (2016). Architecting Data Lakes: Data Management Architectures for Advanced Business Use Cases. O’Reilly Media. Covers scalable data lake architectures, storage strategies, and analytics-driven design patterns.

    [9] Poppe, O., Lei, C., Rundensteiner, E. A., Dougherty, D. J., Deva, G., Fajardo, N., et al. (2017). CAESAR: Context-Aware Event Stream Analytics for Urban Transportation Services. Proceedings of EDBT 2017. Introduces an event-driven stream analytics framework optimized for large-scale real-time data processing.

    [10] Munshi, A. A., & Mohamed, Y. A. R. I. (2018). Data Lake Lambda Architecture for Smart Grids Big Data Analytics. IEEE Access, 6, 40463–40471. Proposes a Lambda-based data lake architecture supporting scalable batch and real-time analytics for smart grid applications.

    [11] Giebler, C., Gröger, C., Hoos, E., & Mitschang, B. (2019). Leveraging the Data Lake: Current State and Challenges. In Big Data Analytics and Knowledge Discovery, Springer, pp. 179–188. Reviews data lake architecture challenges including scalability, metadata management, and analytics integration.

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