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

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