International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Evaluating Apache Spark and Apache Flink for Modern Data Streaming Solutions

Author(s) Varun Garg
Country USA
Abstract Real-time data processing enables swift decisions based on continuous data streams and immediate insights from various data sources, modern data architectures now rely mostly on it. The paper reviews two of the most widely known distributed systems for real-time analytics: Apache Spark and Apache Flink, along with their unique challenges and solutions. This study, through extended comparison analysis, underlines the performance of each framework, use case feasibility, scalability, and state management capability. Apache Flink's real streaming design and strong stateful processing capability show to be helpful for low-latency continuous data streaming. It is thus ideal for use cases calling for considerable dependability and accuracy. Supported by a complete ecosystem that readily links with tools for machine learning and large data, Apache Spark's structured streaming offers considerable batch and micro-batch processing capabilities. The discussion includes increasing trends in machine learning integration, edge computing, and multi-cloud installations, apart from future research possibilities that can enhance real-time data processing systems. This paper discusses how to choose the appropriate framework for their demands concerning scalability, data processing, and strategic goals for future scaling.
Keywords Real-Time Processing, Apache Spark, Apache Flink, Data Streaming, Distributed Frameworks, Low-Latency, State Management, Machine Learning Integration
Published In Volume 3, Issue 4, July-August 2021
Published On 2021-08-25
DOI https://doi.org/10.36948/ijfmr.2021.v03i04.22569
Short DOI https://doi.org/g82h6m
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E-ISSN 2582-2160
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