Refactoring Legacy Batch Jobs into Real-Time Streaming Services

Authors: Raju Dachepally

DOI: https://doi.org/10.5281/zenodo.14850900

Short DOI: https://doi.org/g84qgc

Country: USA

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Abstract: Traditional batch processing systems have been the backbone of enterprise computing for decades, handling large volumes of data through scheduled execution cycles. However, the rise of real-time data processing has made these systems increasingly inadequate for modern business needs, where immediate insights and rapid response times are critical. Migrating from batch-based processing to real-time streaming services enables enterprises to process data continuously, reducing latency, improving decision-making, and enhancing customer experiences. This paper explores the challenges of legacy batch systems, outlines best practices for transitioning to real-time streaming architectures, and discusses the implementation strategies using event-driven technologies such as Apache Kafka, Apache Flink, AWS Kinesis, and Google Pub/Sub. Case studies from industries such as banking, e-commerce, and healthcare highlight the advantages of real-time data processing. Additionally, future trends in real-time stream processing are explored, demonstrating the importance of adopting modern data pipelines for business agility and competitiveness.

Keywords: Batch Processing, Real-Time Streaming, Event-Driven Architecture, Kafka, AWS Kinesis, Apache Flink, Streaming Analytics, Legacy Modernization

Paper Id: 232112

Published On: 2025-01-04

Published In: Volume 13, Issue 1, January-February 2025

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