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Research Paper | Computer Science & Engineering | United States of America | Volume 13 Issue 11, November 2024 | Popularity: 5.9 / 10
Real-time Analytics on AWS and Google Cloud to Unlock Data Driven Insights
Anudeep Kandi, Maria Anurag Reddy Basani
Abstract: This study comprehensively analyzes Amazon Web Services (AWS) and Google Cloud in supporting real-time analytics and machine learning (ML) integration. We utilized transactional data from the Kaggle ?Credit Card Fraud Detection? dataset. The experiment evaluates both platforms? metrics: ingestion latency, data processing efficiency, ML inference latency, scalability, and cost-effectiveness. AWS and Google Cloud were configured with identical virtualized hardware environments to ensure replicable results. Findings show that AWS consistently outperformed Google Cloud. It suppressed with an average ingestion latency of 116.1 ms compared to Google Cloud?s 125.2 ms and a 10.8% faster query processing time. AWS SageMaker also demonstrated a 15.8% reduction in ML inference latency over Google Cloud?s AI Platform. Scalability tests revealed that AWS maintained stable performance at ingestion rates exceeding 2,500 records per second. It surpassed Google Cloud?s limit of 2,000 records per second. Cost analysis further indicated AWS?s marginal cost advantage in data processing and ML inference. Results were validated against baseline metrics from existing literature. It confirms that AWS offers a more efficient, cost-effective solution for real-time, ML-integrated analytics, particularly in high-load environments.
Keywords: Real-time analytics, Amazon Web Services, Google Cloud, ML integration, ingestion latency, data processing efficiency, scalability, cost-effectiveness, cloud-based data analytics, high-frequency data environments
Edition: Volume 13 Issue 11, November 2024
Pages: 302 - 308
DOI: https://www.doi.org/10.21275/SR241105211600
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