ACCELERATING PREDICTIVE ANALYTICS WITH CLOUD-NATIVE EVENT STREAMING
Fuente:
Zenodo
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Recurso digital |
| Publié: |
Zenodo
2021
|
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866901717797306368 |
|---|---|
| author | Dr. Anukesh Rao |
| author_facet | Dr. Anukesh Rao |
| contents | <p><a href="https://ijetrm.com/issues/files/Jun-2021-18-1750268819-JUNE202127.pdf" target="_blank" rel="noopener">Real-time analytics </a>is increasingly critical for timely, informed decision-making across industries. This paper<br>examines how integrating cloud-based event streaming platforms with machine learning enables real-time<br>predictive analytics at scale. We design an architecture leveraging Amazon Kinesis and Apache Kafka for highthroughput data ingestion, combined with AWS Lambda and SageMaker to perform instant predictive inference<br>on streaming data. Key challenges of scalability, fault tolerance, and low latency are addressed through cloudmanaged services and distributed processing techniques. Experimental results demonstrate that our Kinesis-based<br>pipeline handled over one million events per minute with sub-second end-to-end latency, and real-time processing<br>improved predictive accuracy by 20% compared to batch methods. Apache Kafka achieved comparable<br>performance but required more manual tuning to scale. These findings underscore that cloud event streaming,<br>when properly architected, can significantly reduce latency and enhance the accuracy of predictive models during<br>live data processing. The outcomes align with prior research on scalable cloud analytics and illustrate practical<br>strategies for deploying real-time predictive analytics across domains.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15692470 |
| institution | Zenodo |
| language | |
| publishDate | 2021 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | ACCELERATING PREDICTIVE ANALYTICS WITH CLOUD-NATIVE EVENT STREAMING Dr. Anukesh Rao <p><a href="https://ijetrm.com/issues/files/Jun-2021-18-1750268819-JUNE202127.pdf" target="_blank" rel="noopener">Real-time analytics </a>is increasingly critical for timely, informed decision-making across industries. This paper<br>examines how integrating cloud-based event streaming platforms with machine learning enables real-time<br>predictive analytics at scale. We design an architecture leveraging Amazon Kinesis and Apache Kafka for highthroughput data ingestion, combined with AWS Lambda and SageMaker to perform instant predictive inference<br>on streaming data. Key challenges of scalability, fault tolerance, and low latency are addressed through cloudmanaged services and distributed processing techniques. Experimental results demonstrate that our Kinesis-based<br>pipeline handled over one million events per minute with sub-second end-to-end latency, and real-time processing<br>improved predictive accuracy by 20% compared to batch methods. Apache Kafka achieved comparable<br>performance but required more manual tuning to scale. These findings underscore that cloud event streaming,<br>when properly architected, can significantly reduce latency and enhance the accuracy of predictive models during<br>live data processing. The outcomes align with prior research on scalable cloud analytics and illustrate practical<br>strategies for deploying real-time predictive analytics across domains.</p> |
| title | ACCELERATING PREDICTIVE ANALYTICS WITH CLOUD-NATIVE EVENT STREAMING |
| url | https://doi.org/10.5281/zenodo.15692470 |