About the Journal

Aim

The Journal of Scalable Data Engineering and Intelligent Computing, ISSN : 3139-0978, aims to serve as an national platform for publishing innovative research in scalable data infrastructures, intelligent automation, and computation-driven data engineering. The journal focuses on next-generation architectures and algorithms that enable efficient, automated, and AI-enhanced data processing across enterprise and large-scale distributed environments.

Scope

The journal invites high-quality original research articles, reviews, and case studies in the following areas:

  • Scalable data engineering architectures and distributed data systems

  • Intelligent automation and AI-driven workflow optimization

  • Big data management, storage, and processing frameworks

  • Metadata-driven and low-code/no-code data engineering tools

  • High-performance ETL/ELT pipeline design and orchestration

  • Distributed query engines and large-scale data transformation

  • Real-time, streaming, and event-driven data processing

  • Intelligent computing techniques for data engineering

  • Machine learning–enabled data management and prediction

  • Data reliability, quality assessment, lineage, and governance

  • Hybrid cloud, multi-cloud, and edge-to-cloud data engineering

  • Use cases from enterprise analytics, finance, telecom, healthcare, etc.


    Frequency of publication -Three issues per year
    Language -     English
    Subject -     Engineering
    Year of Starting -     2024
    format of publication  -     Online Only
    ISSN -3139-0978