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Review Papers | Computer Science and Information Technology | Volume 13 Issue 9, September 2024 | Pages: 1586 - 1590 | Singapore
Architecting for Real - Time Analytics: Leveraging Stream Processing and Data Warehousing Integration
Abstract: This review explores integrating stream processing frameworks with traditional data warehousing to enhance real - time analytics. Key frameworks such as Apache Kafka, Apache Flink and Apache Storm are analysed for their ability to manage real - time data streams. The review highlights the importance of optimizing data flow, ensuring consistency, and minimizing latency, providing insights into hybrid models that effectively combine real - time and historical data for superior analytics performance.
Keywords: Real - Time Analytics, Stream Processing, Data Warehousing, Apache Kafka, Data Pipelines, Latency Management, Hybrid Architecture
How to Cite?: Puneet Matai, Abir Bhatia, "Architecting for Real - Time Analytics: Leveraging Stream Processing and Data Warehousing Integration", Volume 13 Issue 9, September 2024, International Journal of Science and Research (IJSR), Pages: 1586-1590, https://www.ijsr.net/getabstract.php?paperid=SR24925170923, DOI: https://dx.doi.org/10.21275/SR24925170923