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

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.