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Relevant bibliographies by topics / Flink vs Kafka latency

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Journal articles

Academic literature on the topic 'Flink vs Kafka latency'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Flink vs Kafka latency"

1

Raveendra, Reddy Pasala, Raja Pulicharla Mohan, and Premani Varsha. "Optimizing Real-Time Data Pipelines for Machine Learning: A Comparative Study of Stream Processing Architectures." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 1653–60. https://doi.org/10.5281/zenodo.14948785.

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Within the time of enormous information and real-time analytics, optimizing information pipelines for machine learning is basic for convenient and exact bits of knowledge. This consideration analyzes the execution and versatility of Apache Kafka Streams, Apache Flink, and Apache Pulsar in real-time machine-learning applications. In spite of the wide use of these innovations, there's a need for comprehensive comparative examination with respect to their productivity in commonsense scenarios. This inquiry about addresses this crevice by giving a point-by-point comparison of these systems, center

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2

Researcher. "ADVANCEMENTS IN REAL-TIME STREAM PROCESSING: A COMPARATIVE STUDY OF APACHE FLINK, SPARK STREAMING, AND KAFKA STREAMS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 631–39. https://doi.org/10.5281/zenodo.14216515.

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This article presents a comprehensive comparative analysis of three leading stream processing platforms: Apache Flink, Spark Streaming, and Kafka Streams, examining their architectural approaches, performance characteristics, and operational considerations in real-time data processing scenarios. Through extensive benchmarking and evaluation, we investigated these platforms across multiple dimensions, including processing latency, throughput capacity, resource utilization, and operational complexity. The article reveals that Apache Flink demonstrates superior performance in low-latency sce

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3

Vinnakota, Santosh. "Implementing Schema Evolution in Real-Time Analytics Architectures." International Scientific Journal of Engineering and Management 03, no. 07 (2024): 1–8. https://doi.org/10.55041/isjem02065.

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Abstract—The increasing demand for real-time analytics necessitates robust schema evolution mechanisms to accommodate dynamic changes in data structures without disrupting ongoing operations. This paper explores schema evolution strategies in real-time analytics architectures, highlighting best practices, challenges, and implementation methodologies. We discuss techniques such as schema-on- read, schema registry, and schema migration, supported by modern data streaming frameworks like Apache Kafka, Apache Flink, and Apache Iceberg. Additionally, we provide an implementation framework with prac

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4

BOZKURT, Alper, Furkan EKICI, and Hatice YETISKUL. "Utilizing Flink and Kafka Technologies for Real-Time Data Processing: A Case Study." Eurasia Proceedings of Science Technology Engineering and Mathematics 24 (December 25, 2023): 177–83. http://dx.doi.org/10.55549/epstem.1406274.

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In today's very competitive business world, being able to use data to its fullest in real time has become a key differentiation. This paper looks at how two cutting-edge technologies, Apache Flink and Apache Kafka, work together and how they are changing the way real-time data is processed and analyzed. With its fault-tolerant framework made for collecting data from many sources, Apache Kafka is a leader in reliability and scalability when it comes to ingesting data. Apache Flink is the perfect partner for Kafka because it is great at stream processing and low-latency event handling. This pape

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5

Raveendra Reddy Pasala, Mohan Raja Pulicharla, and Varsha Premani. "Optimizing Real-Time Data Pipelines for Machine Learning: A Comparative Study of Stream Processing Architectures." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 1653–60. http://dx.doi.org/10.30574/wjarr.2024.23.3.2818.

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Abstract:

Within the time of enormous information and real-time analytics, optimizing information pipelines for machine learning is basic for convenient and exact bits of knowledge. This consideration analyzes the execution and versatility of Apache Kafka Streams, Apache Flink, and Apache Pulsar in real-time machine-learning applications. In spite of the wide use of these innovations, there's a need for comprehensive comparative examination with respect to their productivity in commonsense scenarios. This inquiry about addresses this crevice by giving a point-by-point comparison of these systems, center

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6

Sangeeta Rani. "Tools and techniques for real-time data processing: A review." International Journal of Science and Research Archive 14, no. 1 (2025): 1872–81. https://doi.org/10.30574/ijsra.2025.14.1.0252.

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Real-time data processing is an essential component in the modern data landscape, where vast amounts of data are generated continuously from various sources such as Internet of Things devices, social media, financial transactions, and manufacturing systems. Unlike traditional batch processing methods that analyse data in intervals, real-time data processing enables the continuous intake, manipulation, and analysis of data within milliseconds of generation. This capability is critical for applications requiring instant insights and rapid decision-making, including fraud detection, predictive ma

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7

Sampath Kini K. "Exploring Real-Time Data Processing Using Big Data Frameworks." Communications on Applied Nonlinear Analysis 31, no. 8s (2024): 620–34. http://dx.doi.org/10.52783/cana.v31.1561.

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Big data frameworks that weaken the throughput of data processing, allowing for real-time data processing like Apache Spark, Kafka, and Flink are other developments. Regarding quick decisions by each measurement, the scalability, fault tolerance, and latency of three architectures Here each stream processing, lambda, and Kappa have been further studied and measured to approach a conclusion. Based on a methodical survey of literature, performance laws, and case studies, all three frameworks and architectures pros and cons measure us, which can then be used for separate operations use situations

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8

Narendra Reddy Sanikommu. "Real-time stream processing engines: Architectural analysis and implementation considerations." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 3006–16. https://doi.org/10.30574/wjarr.2025.26.2.1916.

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This article provides an in-depth architectural analysis of three leading stream processing engines: Apache Spark Streaming, Apache Flink, and Kafka Streams. As organizations increasingly rely on real-time data processing capabilities to drive decision-making, understanding the fundamental architectural differences between these technologies has become crucial for successful implementation. The analysis explores how Spark Streaming's micro-batch approach prioritizes throughput and integration with the Spark ecosystem, while Flink's true streaming design enables minimal latency and sophisticate

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9

Surya Gangadhar Patchipala. "Real-Time AI Analytics with Apache Flink: Powering Immediate Insights with Stream Processing." World Journal of Advanced Engineering Technology and Sciences 13, no. 2 (2024): 038–50. http://dx.doi.org/10.30574/wjaets.2024.13.2.0539.

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Real-time AI analytics is the latest favorite of Apache Flink, and businesses love what it offers, as the framework has everything to help analyze data as it streams in. With the widespread need for swift, data-driven decision-making, Flink's speed of low latency processing, event timing, and ability to leverage AI models reactively so you have instant insights make it a solid choice. In this article, we will understand Flink's architecture and how it makes stream processing resilient to scalability and failure and builds complex applications like fraud detection and personalization recommenda

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10

Aravind Raghu. "A Novel Java-Based Framework for Real-Time Financial Risk Assessment and Anomaly Detection Using Apache Kafka and Apache Flink." Journal of Information Systems Engineering and Management 10, no. 36s (2025): 1009–18. https://doi.org/10.52783/jisem.v10i36s.6628.

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Modern financial systems create massive amounts of real-time data from high-frequency trading platforms, market feeds, and transactional systems. To address these issues, this paper proposes a novel integrated framework for real-time financial risk assessment and anomaly detection. It is built upon the high-throughput fault-tolerant messaging system Apache Kafka and low-latency stateful stream processor Apache Flink. The framework is built in Java, which guarantees performance, painless integration with enterprise systems, and scalability to meet the future development of market conditions. Ex

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