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Articles de revues sur le sujet « Flink vs Kafka latency »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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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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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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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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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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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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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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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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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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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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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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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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Alang, Karan Singh, and Prof (Dr) Ajay Shriram Kushwaha. "Stream Processing with Apache Kafka: Real-Time Data Pipelines." International Journal of Research in Modern Engineering & Emerging Technology 13, no. 3 (2025): 216–27. https://doi.org/10.63345/ijrmeet.org.v13.i3.13.

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Apache Kafka has emerged as a pivotal technology in the realm of real-time data processing, enabling the construction of robust and scalable stream processing systems. This paper explores the utilization of Apache Kafka as a backbone for real-time data pipelines, detailing its capacity to ingest, buffer, and process continuous streams of data with high throughput and minimal latency. Kafka’s architecture is built on a distributed, fault-tolerant model that leverages partitioned logs to ensure data consistency and availability even amid node failures. By employing a producer–consumer paradigm,

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Rajeeva Chandra Nagarakanti. "Innovations in real-time financial data streaming using cloud technologies." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 1431–43. https://doi.org/10.30574/wjaets.2025.15.2.0615.

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The financial industry has undergone a dramatic transformation with real-time data streaming technologies becoming essential rather than optional. This article explores how cloud-native architectures and open-source technologies are revolutionizing financial services through low-latency processing capabilities. From fraud detection to algorithmic trading and personalized customer experiences, streaming platforms enable financial institutions to make instantaneous decisions based on massive data volumes. The integration of Apache Kafka and Flink within containerized Kubernetes environments has

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Satyanandam Kotha. "Distributed Fake Review Detection and Real-Time Anomaly Detection: A Technical Framework." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 3437–47. https://doi.org/10.32628/cseit25112824.

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This work presents a distributed real-time system for detecting fake reviews on digital platforms, addressing growing challenges to marketplace integrity. Our architecture combines event-driven streaming pipelines (Apache Flink, Kafka Streams, and Spark Streaming) with advanced machine learning to process reviews instantly, enabling detection within 100 milliseconds. The system integrates natural language processing, graph neural networks, and behavioral analytics to identify complex fraud patterns such as bot-generated content, collusive reviewer networks, and coordinated campaigns. A hybrid

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Jatinder Singh. "Real-time Contextual AI for Proactive Fraud Detection in Consumer Lending: Architectures, Algorithms, and Operational Challenges." Journal of Information Systems Engineering and Management 10, no. 4 (2025): 1556–71. https://doi.org/10.52783/jisem.v10i4.10993.

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Consumer lending faces an existential threat from increasingly sophisticated fraud tactics, with synthetic identity fraud alone causing $6.8 billion in losses in 2024 (FDIC). Traditional rule-based systems fail to detect 72% of emerging fraud patterns (Javelin 2025). This paper presents a comprehensive framework for real-time contextual AI systems that reduce false positives by 40% while detecting 95% of sophisticated fraud within 300ms. We detail architectures combining streaming data pipelines (Apache Flink, Kafka), low-latency feature engineering, and ensemble AI models (GNNs, transformer-b

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Traub, Jonas, Philipp Marian Grulich, Alejandro Rodríguez Cuéllar, et al. "Scotty." ACM Transactions on Database Systems 46, no. 1 (2021): 1–46. http://dx.doi.org/10.1145/3433675.

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Window aggregation is a core operation in data stream processing. Existing aggregation techniques focus on reducing latency, eliminating redundant computations, or minimizing memory usage. However, each technique operates under different assumptions with respect to workload characteristics, such as properties of aggregation functions (e.g., invertible, associative), window types (e.g., sliding, sessions), windowing measures (e.g., time- or count-based), and stream (dis)order. In this article, we present Scotty , an efficient and general open-source operator for sliding-window aggregation in st

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Qiao, Yu, Kaixian Xu, and Alan Wilson. "Real-Time Personalized Ad Recommendation Based on User Behavioral Analysis." Artificial Intelligence Advances 7, no. 1 (2025): 10–21. https://doi.org/10.30564/aia.v7i1.9761.

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Real-time personalized ad recommendation systems are crucial for enhancing user engagement and satisfaction. To address the challenge of delivering highly relevant ads in a dynamic, large-scale environment, this paper proposes a novel approach that integrates real-time user behavior analysis with advanced time series modeling and stream processing techniques. Specifically, the system leverages Long Short-Term Memory (LSTM) networks to capture both short-term and long-term user preferences, ensuring accurate and personalized ad recommendations. By utilizing stream processing frameworks like Apa

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Raju, Dachepally. "Refactoring Legacy Batch Jobs into Real-Time Streaming Services." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 13, no. 1 (2025): 1–6. https://doi.org/10.5281/zenodo.14850900.

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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

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Murugan, Lakshmanan. "Designing and Optimizing Scalable, Cloud-Native Data Pipelines for Real-Time Analytics: A Comprehensive Study." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 12 (2025): 2085–91. https://doi.org/10.5281/zenodo.14591136.

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Modern enterprises increasingly require sub- second insights derived from massive, continuously  generated data streams. To achieve these stringent  performance goals, organizations must architect cloud- native data pipelines that integrate high-throughput  messaging systems, low-latency streaming engines, and elastically scalable serving layers. Such pipelines must handle millions of events per second, enforce strict latency budgets, comply with data protection laws (e.g., GDPR, CCPA), adapt to evolving schemas, and continuously scale resources on demand. This paper offers a co

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Piyush Dubey. "Data Lake Architecture at Uber: A Lambda-Based Approach to Real-Time and Batch Analytics with Cross-Industry Perspectives." Journal of Computer Science and Technology Studies 7, no. 7 (2025): 325–32. https://doi.org/10.32996/jcsts.2025.7.7.35.

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The evolution of data infrastructure in modern transportation platforms demonstrates the critical role of Lambda architecture in addressing the dual challenges of real-time processing and comprehensive historical analytics. Through the implementation of sophisticated data lake architectures leveraging open-source technologies, including Apache Kafka for streaming, Apache Flink for real-time processing, Apache Hudi for data lake management, and Presto for distributed querying, organizations achieve significant reductions in data freshness latency while maintaining scalability. The architectural

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Ruby Dinakar. "Real-Time IoT Sensor Data Streaming and Processing with Apache Flink: A Scalable Solution for Smart Monitoring." Journal of Electrical Systems 20, no. 11s (2024): 3175–81. https://doi.org/10.52783/jes.8042.

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The Internet of Things (IoT) has revolutionized data-driven decision-making by enabling real-time data acquisition through an extensive network of sensors. However, the massive influx of continuous, high-velocity sensor data poses significant challenges for traditional data. The rapid explosion of sensor data, necessitating robust, scalable real-time data processing solutions. This paper presents a comprehensive solution for ingestion, processing, and analysis of large-scale sensor data streams in real time, based on Apache Kafka and Apache Flink. The framework handles continuous data flows wi

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SANDEEP PAMARTHI. "Real-time state management techniques using RocksDB: A high-performance approach to scalable stream processing." International Journal of Science and Research Archive 12, no. 1 (2024): 3180–90. https://doi.org/10.30574/ijsra.2024.12.1.0867.

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The proliferation of real-time artificial intelligence (AI) and machine learning (ML) systems has amplified the demand for robust, low-latency state management techniques capable of operating at scale. From streaming feature extraction to online model inference and complex event processing, stateful operations lie at the core of intelligent data-driven pipelines. However, managing this state in distributed environments presents numerous challenges, including fault tolerance, efficient recovery, incremental updates, and tight latency budgets. This paper explores RocksDB, a high-performance, emb

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Tahir, Jawad, Ruben Mayer, Christoph Doblander, and Hans-Arno Jacobsen. "How Reliable are Streams? End-to-End Processing-Guarantee Validation and Performance Benchmarking of Stream Processing Systems." Proceedings of the VLDB Endowment 18, no. 3 (2024): 585–98. https://doi.org/10.14778/3712221.3712227.

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Stream processing systems (SPSs) provide processing guarantees to ensure reliability under failure. However, no related work exists that empirically validates these guarantees. In this paper, we present PGVal, a tool that can end-to-end validate guarantees of SPSs. Additionally, we introduce new metrics for SPSs, such as reliability, reliable throughput, and failure cost, in addition to a refined definition of latency that results in improved measurements. We benchmark three popular SPSs, namely Kafka Streams, Apache Storm , and Apache Flink. Our results show that the reliability of SPSs depen

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Madhuranthakam, Reddy Srikanth. "Scalable Data Engineering Pipelines for Real-Time Analytics in Big Data Environments." FMDB Transactions on Sustainable Computing Systems 2, no. 3 (2024): 154–66. https://doi.org/10.69888/ftscs.2024.000262.

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With the world becoming increasingly data-driven, actionable insights and decisions that depend on real-time analytics have been at the forefront. However, processing huge volumes of data in real time requires very strong, scalable, and efficient data engineering pipelines. This paper describes the design, development, and optimization of scalable data engineering pipelines for real-time analytics in big data environments. Ingestion, processing, storage and visualization, along with their interactions within the distributed computing setup, will all be part of the paper. More best practices wi

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Odogwu, Rosebenedicta, Jeffrey Chidera Ogeawuchi, Abraham Ayodeji Abayomi, Oluwademilade Aderemi Agboola, and Samuel Owoade. "Real-Time Streaming Analytics for Instant Business Decision-Making: Technologies, Use Cases, and Future Prospects." Journal of Frontiers in Multidisciplinary Research 4, no. 1 (2023): 381–89. https://doi.org/10.54660/.jfmr.2023.4.1.381-389.

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In today’s hyper-competitive and data-driven economy, organizations require instantaneous insights to drive strategic decisions. Real-time streaming analytics (RTSA) has emerged as a transformative paradigm, enabling the continuous ingestion, processing, and analysis of high-velocity data streams from heterogeneous sources. This paper investigates the core technologies underpinning RTSA—such as Apache Kafka, Spark Streaming, Flink, and cloud-native event processing architectures—while exploring the integration of artificial intelligence and machine learning models for predictive insights. Draw

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Alam, Md Ashraful, Ashrafur Rahman Nabil, Abdul Awal Mintoo, and Ashraful Islam. "Real-Time Analytics In Streaming Big Data: Techniques And Applications." Non human journal 1, no. 01 (2024): 104–22. https://doi.org/10.70008/jeser.v1i01.56.

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The increasing prevalence of streaming big data has revolutionized how organizations approach real-time analytics, providing a competitive edge by enabling immediate, actionable insights from continuously generated data streams. This review systematically examines real-time analytics techniques and their applications in streaming big data using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. The methodology involves identifying, screening, and synthesizing relevant studies to provide a comprehensive overview of state-of-the-art techniques, including d

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Ritesh, Kumar. "Data Engineering in the Era of Real-Time Analytics: Tools, Techniques, and Architectural Patterns." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 11, no. 4 (2023): 1–17. https://doi.org/10.5281/zenodo.15086602.

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The rapid evolution of real-time analytics has fundamentally reshaped modern data engineering practices. As organizations increasingly prioritize low-latency data processing, there is a notable shift from batch-oriented architectures to streaming-based approaches that enable continuous data ingestion, transformation, and analysis. This paper examines the key tools, techniques, and architectural patterns that facilitate real-time data processing at scale. A comparative analysis of Lambda and Kappa architectures is presented, highlighting their design principles, performance trade-offs, and impl

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Sanjay Lote, Praveena K B, and Durugappa Patrer. "Real-time data stream processing in large-scale systems." World Journal of Advanced Research and Reviews 15, no. 3 (2022): 560–70. https://doi.org/10.30574/wjarr.2022.15.3.0903.

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Real-time data stream processing has emerged as a crucial element in modern large-scale systems, facilitating rapid decision-making and real-time analytics across various domains. As data volumes continue to grow exponentially, the need for efficient, scalable, and fault-tolerant stream processing solutions has become more pressing. This paper provides a comprehensive exploration of real-time data processing architectures, highlighting key components such as distributed stream processing frameworks, parallel data pipelines, and event-driven computing models. The study delves into state-of-the-

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Arjun, Reddy Lingala. "Stream Processing Internals and Usecases." International Journal of Leading Research Publication 5, no. 4 (2024): 1–7. https://doi.org/10.5281/zenodo.14945841.

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Batch processing is widely used concept in data warehousing where many companies build analytical solutions deriving insights into their systems and building new products based on the analysis based on various aspects of the systems. The exponential growth of real-time data sources like IoT sensors, social media has necessitated systems capable of processing unbounded data streams with low latency, high throughput, and guaranteed correctness. Unlike batch processing, stream process- ing engines must handle continuous data flows with dynamic arrival patterns, out-of-order events, and variable w

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Mokale, Mahesh. "Best practices for real-time data processing in media applications." International Journal of Multidisciplinary Research and Growth Evaluation 1, no. 5 (2020): 131–34. https://doi.org/10.54660/.ijmrge.2020.1.5.131-134.

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In an era dominated by digital content consumption, media applications face unprecedented challenges in processing and managing immense volumes of data in real time. Users now demand not only seamless streaming experiences but also hyper-personalized interactions tailored to their unique preferences. This increasing demand extends across various scenarios, from live video streaming and interactive media platforms to personalized content recommendations and immersive virtual environments. To meet these high expectations, media applications must overcome limitations inherent in traditional data

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Emmanuel Cadet, Olajide Soji Osundare, Harrison Oke Ekpobimi, Zein Samira, and Yodit Wondaferew Weldegeorgise. "AI-powered threat detection in surveillance systems: A real-time data processing framework." Open Access Research Journal of Engineering and Technology 7, no. 2 (2024): 031–45. http://dx.doi.org/10.53022/oarjet.2024.7.2.0057.

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The increasing need for enhanced security has driven the adoption of AI-powered threat detection in surveillance systems. Traditional surveillance methods, reliant on manual monitoring, are often inefficient in detecting complex, evolving threats in real time. This review proposes a comprehensive real-time data processing framework for AI-powered threat detection in surveillance systems, designed to automate and optimize threat identification, classification, and response. The framework integrates AI algorithms, including machine learning and deep learning models, to analyze vast amounts of su

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Banerjee, Somnath. "Modernizing Healthcare Master Data Management (MDM): Harnessing Real-Time Processing, IoT, and Blockchain." International Journal of Computing and Engineering 7, no. 4 (2025): 24–39. https://doi.org/10.47941/ijce.2808.

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Purpose: This paper aims to propose a robust, future-ready master data management (MDM) architecture that addresses traditional MDM challenges by enhancing data accuracy, trust, and accessibility, while aligning with regulatory frameworks such as the 21st Century Cures Act and TEFCA. Methodology: Employing a qualitative design-oriented approach, the study combines literature review, technical architecture modeling, and real-world case analyses. Key interoperability standards (HL7/FHIR), decentralized identity protocols (DIDs), and smart contract frameworks were analyzed. The proposed architect

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Devarasetty, Narendra. "Scalable Data Engineering Approaches For Ai-Driven Industrial Iot Applications." International Journal of Scientific Research and Management (IJSRM) 11, no. 06 (2023): 954–68. https://doi.org/10.18535/ijsrm/v11i06.ec3.

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The Industrial Internet of Things (IIoT) represents a transformative shift in modern industries, enabling seamless interconnectivity among devices, systems, and processes. By integrating advanced data analytics and interconnected systems, IIoT facilitates the optimization of operations, cost reduction, and enhancement of decision-making processes. When combined with Artificial Intelligence (AI), these capabilities are exponentially amplified, offering predictive insights, real-time monitoring, and automation of intricate tasks. This fusion of IIoT and AI heralds unprecedented opportunities for

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Arjun Sirangi. "Retail Fraud Detection via Log Analysis and Stream Processing." Computer Fraud and Security, April 30, 2018. https://doi.org/10.52710/cfs.678.

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Retail fraud has evolved into a sophisticated threat in the digital age, necessitating advanced detection mechanisms that leverage real-time data processing. This paper presents a technical framework for detecting retail fraud by combining log analysis with stream processing technologies. We address challenges such as scalability, latency, and concept drift through a hybrid architecture that integrates anomaly detection algorithms (e.g., clustering, graph-based models) with distributed stream processing engines (e.g., Apache Flink, Kafka). Evaluations demonstrate that our approach achieves an

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ANGBERA, ATURE, and HUAH CHAN. "A NOVEL TRUE REAL-TIME SPATIOTEMPORAL DATA STREAM PROCESSING FRAMEWORK." Jordanian Journal of Computers and Information Technology, 2022, 1. http://dx.doi.org/10.5455/jjcit.71-1646838830.

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The ability to interpret spatiotemporal data streams in real-time is critical for a range of systems. However, processing vast amounts of spatiotemporal data out of several sources, such as online traffic, social platforms, sensor networks, and other sources, is a considerable challenge. The major goal of this study is to create a framework for processing and analyzing spatiotemporal data from multiple sources with irregular shapes so that researchers can focus on data analysis instead of worrying about the data sources' structure. We introduced a novel spatiotemporal data paradigm for true-re

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