International Journal on Science and Technology

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Combining Batch and Stream Processing for Hybrid Data Workflows

Author(s) Santosh Vinnakota
Country United States
Abstract The exponential growth of data has necessitated the development of hybrid data workflows that leverage both batch and stream processing. Traditional batch processing is ideal for large-scale historical data analysis, while stream processing excels at real-time event-driven analytics. This paper explores the integration of these paradigms to create hybrid data workflows that enable real-time decision-making while ensuring data accuracy and consistency. We discuss architectures, frameworks, use cases, and challenges associated with hybrid data workflows, offering insights into best practices for implementation.
Keywords Hybrid Data Processing, Batch Processing, Stream Processing, Lambda Architecture, Event Sourcing
Field Engineering
Published In Volume 15, Issue 1, January-March 2024
Published On 2024-02-07
Cite This Combining Batch and Stream Processing for Hybrid Data Workflows - Santosh Vinnakota - IJSAT Volume 15, Issue 1, January-March 2024. DOI 10.71097/IJSAT.v15.i1.2819
DOI https://doi.org/10.71097/IJSAT.v15.i1.2819
Short DOI https://doi.org/g899f8
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