Designing and Optimizing Scalable, Cloud-Native Data Pipelines for Real-Time Analytics: A Comprehensive Study

Murugan Lakshmanan1

1

Publication Date: 2025/01/04

Abstract: Modern enterprises increasingly require sub- second insights derived from massive, continuously generated data streams. To achieve these stringent performance goals, organizations must architect cloud- native data pipelines that integrate high-throughput messaging systems, low-latency streaming engines, and elastically scalable serving layers. Such pipelines must handle millions of events per second, enforce strict latency budgets, comply with data protection laws (e.g., GDPR, CCPA), adapt to evolving schemas, and continuously scale resources on demand. This paper offers a comprehensive examination of the principles, patterns, and operational techniques needed to design and optimize cloud-native data pipelines for real-time analytics. We present a reference architecture that unifies messaging platforms (e.g., Apache Kafka), stream processing frameworks (e.g., Apache Flink), and serving tiers (e.g., OLAP databases) orchestrated by Kubernetes. We introduce theoretical models for throughput, latency, and cost; discuss strategies for auto scaling, CI/CD, observability, and disaster recovery; and address compliance, governance, and security requirements. Advanced topics—including machine learning-driven optimizations, edge computing architectures, interoperability standards (e.g., Cloud Events), and data mesh paradigms—provide a forward- looking perspective. Supported by empirical evaluations, performance metrics tables, formulas, and placeholders for illustrative figures and charts, this paper serves as a definitive resource for practitioners and researchers building next-generation, cloud-native, real-time data pipelines.

Keywords: Cloud-Native Computing, Real-Time Analytics, Data Streaming, Messaging Platforms, Scalability, Data Governance, Machine Learning, Kubernetes, Compliance.

DOI: https://doi.org/10.5281/zenodo.14591136

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24DEC1504.pdf

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