Modernizing Data Engineering: Leveraging Advanced Distributed Frameworks, Hybrid Storage Solutions, and Machine Learning Driven Architectures
Year: 2025
Type: article
Abstract: In today's rapidly evolving data engineering landscape, professionals must continuously adapt to emerging technologies and methodologies to build efficient, scalable, and resilient systems. This article explores cutting-edge innovations across key domains, including distributed processing frameworks, database architectures, API evolution, workflow orchestration, containerization, and the convergence of data engineering with machine learning. By examining advancements in technologies such as Apache Spark, hybrid SQL/NoSQL databases, GraphQL, Airflow, Kubernetes, and cloud-native architectures, we provide a comprehensive overview of how these developments are reshaping the field. The integration of these technologies is enabling more automated, performant, and secure data pipelines while simultaneously addressing growing demands for real-time processing, compliance, and cost optimization in modern data ecosystems.
Author: Naveen Srikanth Pasupuleti
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410271059