Combining Batch and Stream Processing for Hybrid Data Workflows
Santosh Vinnakota · International Journal on Science and Technology · 2024
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.