REAL-TIME DATA ENGINEERING AND AI-DRIVEN ANALYTICS: A UNIFIED FRAMEWORK FOR INTELLIGENT STREAM PROCESSING AND PREDICTIVE MODELING
DOI:
https://doi.org/10.34218/IJCET_15_02_026Keywords:
Stream Processing, Real-Time Analytics, MLOps, Data Pipeline Orchestration, Apache Kafka, Apache Spark, Machine Learning Engineering, Data GovernanceAbstract
The exponential growth of real-time data streams from IoT devices, social media platforms, and enterprise applications has created unprecedented challenges in data engineering and artificial intelligence implementation. This paper presents a comprehensive framework for real-time data engineering that integrates stream processing, machine learning operations (MLOps), and intelligent analytics to enable scalable, fault-tolerant, and adaptive data pipelines. Our approach combines Apache Kafka for distributed streaming, Apache Spark for real-time processing, and TensorFlow Extended (TFX) for production-grade machine learning workflows. Through empirical evaluation across multiple industry use cases, our framework demonstrates a 78% reduction in data processing latency, 92% accuracy in real-time anomaly detection, and 85% improvement in model deployment efficiency. This research establishes a new paradigm for intelligent data engineering that enables organizations to harness the full potential of real-time analytics and AI-driven decision making.
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Copyright (c) 2024 Praveen Kumar Reddy Gujjala (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
