Optimizing Economic Policy and Sectoral Management with Big Data Technologies: A Case Study of Stream Processing and Machine Learning Frameworks

Abstract

The staggering volume of big data is creating new opportunities while reshaping economic policy formulation, sectoral management, as well as public and private management. The scope of this paper is to examine the impact stream processing frameworks (Apache Flink, Apache Kafka), distributed storage systems (Hadoop, Spark), and more recent machine learning (ML) algorithms, and big data technologies have had on policy impact, forecasting, and compliance for regulation optimization. We showcase computational case studies on fiscal policy optimization, risk analysis in finance, and allocation of agricultural subsidies to show how responsive strategic planning can be improved using scalable big data infrastructure. The research uses a hybrid approach involving improved feature engineering and a parallelized model deployment strategy to create low-latency, high-throughput decision-making systems. This paper contributes by introducing a new optimization approach for real-time streaming data ingestion and classification under limited resources constrained environments using multiple datasets from economics as benchmarks. This paper not only ventures to show what is technologically possible today but also aims to inform future endeavors on how to responsibly, privately, and efficiently compute solutions in a data-centric economy.