Cloud-Enabled Data Analytics: A Critical Review of Trends and Technologies
Raghav Saboo1, Prakhar Gupta2, Pushpendra Singh3 ,Assist. Prof. Varsha Kothari4
1 Student, Dept. of Computer Science Engineering, Medi-Caps University, Madhya Pradesh, India
2 Student, Dept. of Computer Science Engineering, Medi-Caps University, Madhya Pradesh, India.
3 Student, Dept. of Computer Science Engineering, Medi-Caps University, Madhya Pradesh, India.
4Assistant Professor, Dept. of Computer Science Engineering, Medi-Caps University, Madhya Pradesh, India.
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Abstract -Cloud-enabled data analytics is the process of analyzing large data sets using cloud computing resources. It has become increasingly popular due to the vast amount of data that businesses generate and collect. This review paper aims to provide an overview of the current state of cloud-enabled data analytics and its applications.The paper begins with a discussion of the basics of cloud computing and its role in enabling data analytics. It then covers the various types of cloud computing models, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), and how each model can be used for data analytics.Next, the paper explores the different types of data analytics techniques that can be used in the cloud, including descriptive, predictive, and prescriptive analytics. It also covers the tools and technologies that are commonly used in cloud-based data analytics, such as Apache Hadoop, Spark, and NoSQL databases.The paper then discusses the benefits of cloud-enabled data analytics, including cost savings, scalability, and flexibility. It also covers some of the challenges associated with cloud-enabled data analytics, such as data privacy and security concerns.Finally, the paper provides an overview of some of the applications of cloud-enabled data analytics in various industries, including healthcare, finance, and retail. It also discusses some of the emerging trends in cloud-enabled data analytics, such as edge computing and machine learning.
Key Words:Cloud computing, Data analytics, Big data, Artificial intelligence, Distributed computing
