Rate the Article: Architecting for Real - Time Analytics: Leveraging Stream Processing and Data Warehousing Integration, IJSR, Call for Papers, Online Journal
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

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Review Papers | Computer Science and Information Technology | Singapore | Volume 13 Issue 9, September 2024 | Rating: 6 / 10


Architecting for Real - Time Analytics: Leveraging Stream Processing and Data Warehousing Integration

Puneet Matai, Abir Bhatia


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


Edition: Volume 13 Issue 9, September 2024,


Pages: 1586 - 1590



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