AI-Driven Data Pipelines in Cloud Environments

Authors: Srinivasa Kalyan Vangibhuratha

DOI: https://doi.org/10.37082/IJIRMPS.v13.i2.232442

Short DOI: https://doi.org/g9g7v3

Country: USA

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Abstract: In today's data-driven world, organisations face the challenge of managing vast amounts of data effectively. While essential for data management, traditional data pipelines tend to struggle with scalability, slow processing, and manual intervention, limiting their efficiency in cloud environment. To address these challenges, the integration of Artificial Intelligence (AI) into cloud-based data pipelines presents a promising solution. AI-driven data pipelines automate critical processes such as resource allocation, anomaly detection, real-time analytics, and error handling thus significantly improving scalability, cost-efficiency, performance among others. This paper explores the role of AI-driven data pipelines in cloud environments by exploring its benefits, used cases, emerging trends, research directions and future challenges.
From the research, potential advantages of integrating AI into data pipelines include; automation, real-time data processing and analytics, intelligent resource allocation and monitoring security breaches. To realise these advantages, critical components of AI data pipelines are needed; data ingestion, data processing and transformation, machine learning model integration, data storage and retrieval, monitoring and optimisation. By examining key used cases in e-commerce, healthcare, and finance, the paper demonstrated how AI-driven data pipelines enhances decision-making, operational efficiency and unlocked new opportunities. Some of the emerging trends in AI data pipelines pertains to shift toward autonomous data pipelines, growing demand for real-time analytics, integration with edge computing and AI and shift towards serverless computing and Function-as-a-Service (FaaS) architectures. Despite the advantages, key challenges such as data privacy and security, data integration and standardisation, model bias and complexity of model deployment and maintenance remain.

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Paper Id: 232442

Published On: 2025-04-29

Published In: Volume 13, Issue 2, March-April 2025

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