Real-Time Data Orchestration in Cloud Environments: Optimizing Pipelines for Scalable Analytics

Authors: Shafeeq Ur Rahaman

DOI: https://doi.org/10.5281/zenodo.14352082

Short DOI: https://doi.org/g8t7mt

Country: India

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Abstract: The growing reliance on cloud-based systems to handle scalable, real-time analytics demands advanced methods for dynamic management and orchestration of data flows. This article work targets the development of new strategies in cloud environments for real-time data orchestration among them, pipeline optimization is the primary concern to efficiently process large-scale, diverse streams of data. It underlines challenges related to low latency, high throughput, and fault tolerance in distributed systems. It proposes a framework that combines automation, AI-driven decision-making and adaptive resource allocation for effective workflow in data ingestion, transformation, and delivery. It leverages containerization, micro services, and event-driven architectures to enable frictionless scalability and flexibility easily. Empirical evaluations validate the ability of the framework to reduce processing times, improve data reliability, and provide support for real-time analytics on diverse cloud platforms. This thus, becomes a pointer towards good orchestration techniques for unlocking the full potential of cloud analytics by industries on finance, healthcare, and electronic commerce.

Keywords: Real-time data orchestration, cloud computing, scalable analytics, data pipelines, adaptive resource allocation, distributed systems, low latency, high throughput, fault tolerance, event-driven architecture, micro services, and data reliability

Paper Id: 231789

Published On: 2020-09-02

Published In: Volume 8, Issue 5, September-October 2020

Cite This: Real-Time Data Orchestration in Cloud Environments: Optimizing Pipelines for Scalable Analytics - Shafeeq Ur Rahaman - IJIRMPS Volume 8, Issue 5, September-October 2020. DOI 10.5281/zenodo.14352082

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