Comparative Analysis of Public Cloud Providers for Big Data Analytics: AWS, Azure, and Google Cloud
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P103Keywords:
Big Data Analytics, Cloud Computing, Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), Performance Benchmarking, Cost Optimization, Security and Compliance, Multi-Cloud Strategies, Artificial Intelligence, Enterprise Data ProcessingAbstract
In the digital era we are witnessing an exponential growth of data which has made a need for organizations to adopt Cloud Based big data analytics solution, to leverage a scalable, cost effective and a flexible computing infrastructure. Of all the leading cloud service providers, Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP) provide a lot of choice when it comes to big data analytics tools to suit the needs of various businesses and research. In this study a comprehensive comparative analysis of AWS, Azure and Google Cloud from big data analytics point of view is made and their feature offerings, performance, pricing, security and scalability are investigated. To this effect, the use of a mix of qualitative and quantitative research methodologies is done including literature reviews, experimental benchmarking, and case studies on the real world adoption of cloud by big enterprises like Netflix (AWS), BMW (Azure) and Spotify (Google Cloud). From this findings, we can see that each of the cloud provider has their own strengths: AWS is good in to process large scale of data and to integrate with enterprise, Azure gives us a great experience on integration with Microsoft products and rich compliance frameworks, as well as Google Cloud shows superiority in real time data processing and AI powered analytics. By differently framing the question posed above, this research provides good insights for organizations to have a cloud adoption optimization strategy based on the workload demands, cost efficiency and security. It also points to developing trends such as hybrid and multi-cloud strategies, sustainability of cloud computing and AI security monitoring. The study ends with suggestions to the enterprises, policymakers and researchers to choose the most appropriate cloud platform for big data analytics and provides future directions to improve the cloud performance and cost efficiency
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