AI-Driven Data Engineering Pipelines for High-Velocity Analytics Using Serverless Architectures and Event-Stream Intelligence
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I3P109Keywords:
AI-Driven Data Engineering, Serverless Computing, Event-Stream Processing, High-Velocity Analytics, Real-Time Data Pipelines, Cloud-Native Analytics, Intelligent Data OrchestrationAbstract
With a rapid increase in digital ecosystems, new amounts of high-velocity data have been created by Internet of Things (IoT) devices, mobile applications, cloud native microservices, social platforms, and cyber-physical systems volume like never before. Real-time and near-real-time analytics are becoming more crucial to modern enterprises to extract actionable insights and assist or autonomously decide on benefit to support responsive digital services. Nevertheless, the conventional paradigms of data engineering systems, which are typically centered around batch-oriented Extract Transform Load (ETL) systems, inherently lack the flexibility of real-time data rates, strict infrastructure proving and strict resource assignment. These constraints reduce scalability, raise the cost of operation and slow the creation of insight in environments that are latency sensitive. The paper provides an all-inclusive discussion about AIs-based pipelines used in high-velocity analytics, based on the concept of serverless computing and event-stream intelligence. Combining artificial intelligence methods with the direct implementation in data ingestion, transformation, and orchestration and optimization layers allows the proposed approach to support adaptive and self-optimizing pipelines that are able to support heterogeneous streaming workloads. Serverless architectures are characterized by elasticity, cost-efficiency, and fault-tolerance whereas event-stream platforms are characterized by low-latency, high throughput continuous data processing. The paper proposes a theoretical and methodology, which is a machine learning-based workload prediction combined with intelligent stream routing, adaptive schema evolution, and autonomous quality assurance. The models of formal costs and latency are created to have measures of performance benefits in comparison to traditional architectures. The throughput, latency reduction, and operational cost are improved significantly as illustrated in experimental assessment of high-velocity workloads simulated conditions. The findings affirm that AI-based serverless pipelines form a practical and scalable base of the next-generation real-time analytics systems in data-intensive business organizations
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