Real-Time Data Engineering for Smart Applications

Abstract

The digital transformation era is characterized by intelligent applications that increasingly rely on rapid processing and analysis of large, non-homogeneous data streams. These smart systems are built on real-time data engineering, enabling seamless data ingestion, transformation, and decision-making across industries such as healthcare, transportation, manufacturing, and finance. The paper will look into the architectural principles, enabling technologies, practical uses, and the future trend of real-time data engineering. It underscores that frameworks of stream processing, edge computing, and cloud-native processing and AI integration are critical to the development of scalable and responsive intelligent systems. The paper also lists popular traps like latency, scalability, data quality, and security, and also provides a projection of the future trend, which has been brought about by federated learning, data meshes, and quantum computing. The provided insights make a detailed picture of the development of the operational and strategic landscape of smart technologies in real-time by applying data engineering.