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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Analysis Study Research Paper | Computer Science & Engineering | India | Volume 9 Issue 2, February 2020 | Popularity: 5.4 / 10


     

Architecting Real-Time Big Data Analytics: An AWS-Powered Framework Integrating AI and ML for Predictive Insights

Sai Tarun Kaniganti


Abstract: In the digital era, data generation from various sources such as IoT devices, social networks, and transactions has significantly increased, necessitating efficient management solutions. AWS offers a comprehensive real-time data analytics system incorporating Amazon Kinesis, Amazon S3, Amazon DynamoDB, and Amazon SageMaker to handle this influx effectively. By utilizing these tools, organizations can process and analyze data in real-time, aiding in immediate decision-making crucial for sectors like finance, manufacturing, and retail. The integration of AI and ML enhances predictability, allowing for advanced data analysis and timely insights, which improves operational efficiency and customer experience. This architecture includes layers for data ingestion, processing, storage, machine learning integration, and visualization, all designed to support scalable and real-time data analytics. The frameworks implementation has demonstrated significant improvements in operational responsiveness and decision-making speed, proving essential for maintaining market competitiveness and fostering innovation.


Keywords: real-time data analytics, AWS, big data management, AI integration, machine learning


Edition: Volume 9 Issue 2, February 2020


Pages: 1931 - 1940



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Sai Tarun Kaniganti, "Architecting Real-Time Big Data Analytics: An AWS-Powered Framework Integrating AI and ML for Predictive Insights", International Journal of Science and Research (IJSR), Volume 9 Issue 2, February 2020, pp. 1931-1940, https://www.ijsr.net/getabstract.php?paperid=SR24716230925

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