Abstract
Cloud analytics is the process of using cloud computing resources and technologies to analyze and gain insights from large and complex data. Cloud analytics has become increasingly popular in recent years due to its scalability, cost-efficiency, and accessibility. It enables organizations to process large datasets, perform complex analytics, and make data-driven decisions more effectively and affordably. This book chapter provides a comprehensive overview of cloud analytics, covering its fundamental principles, significance in today's data-driven landscape, benefits and challenges of adoption, critical tools and technologies, data management in cloud environments, and promising future directions and emerging trends. By the end of this chapter, readers will have a deep understanding of cloud analytics and its potential to revolutionize the way organizations analyze and leverage data.
Emerging Trends in Cloud Computing Analytics, Scalability, and Service Models
TopIntroduction
Cloud analytics is a framework for using cloud technologies to store, process and analyze large and complex data (Achar et al., 2015). Cloud analytics provides fast and efficient data processing, enables real-time analysis, and ensures data security. Its popularity is growing due to its many advantages, including significantly enhancing production quality (Ajah, I.A. et al., 2019).
In the present age,Cloud analytics has many applications, including data warehousing, big data technologies, data visualization, business intelligence, data security, serverless computing, and more. (Ruiz et al., 2021). At the core of cloud analytics are fundamental services that enable organizations to control the full potential of cloud computing resources and advanced analytics tools for efficient data insight extraction. These principles encompass the following critical components.
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Scalability empowers organizations to allocate resources as needed without substantial upfront investments.
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The cost-efficient pay-as-you-go model ensures organizations only pay for actively used resources, enhancing cost-effectiveness.
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Accessibility is another foundation, allowing users to access cloud analytics solutions from anywhere with an internet connection, facilitating remote collaboration
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Cloud providers guarantee speed and performance with robust infrastructure and advanced processing capabilities.
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Streamlined data integration simplifies data collection, which is necessary for applications requiring immediate insights.
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Predictive analytics, data visualization and business intelligence tools improve decision-making.
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Easy maintenance and disaster recovery capabilities enhance the effectiveness of cloud analytics, allowing organizations to excel in their operations and maintain a competitive edge.
Cloud analytics presents a transformative solution in the present data-driven landscape, where data plays a central role in decision-making and gaining a competitive edge. (Henke et al., 2016). It helps to efficiently collect, process, and analyse data, empowering organizations to make real-time, data-driven decisions (Niu, Y, et al., 2021). In addition, the scalability and cost-effectiveness of Cloud Analytics make it accessible to businesses of all sizes, democratizing data analytics. Cloud Analytics enables organizations to adapt to dynamic market conditions, gain deeper insights into their operations, and enhance competitiveness. With data security and easy maintenance, it offers a secure and low-maintenance solution for data analytics (Ranjan et al.,2021). In a world where data is generated at an unprecedented rate, cloud analytics is essential for harnessing data for strategic and operational advantages.
TopBackground
In the current environment, there is a dominant trend where individuals and businesses are primarily engaged in a data-driven ecosystem, where data plays an integral role in the vast majority of their professional activities and decision-making problems (Carillo, K. D. A. 2017). To cope with the challenges posed by the exponential growth of data, the adoption of cloud analytics emerges as a pivotal strategy, serving as a means to extract invaluable insights and secure a competitive advantage. Cloud analytics stands as the convergence of cloud computing and data analytics, signifying a transformative shift in the methods employed for data processing, storage, and utilization for informed decision-making (Smith, S. 2022).
Key Terms in this Chapter
Edge Computing: Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed.
Elastic compute clusters: These clusters scale up or down on demand to meet the needs of the application.
Deep Learning (DL): Deep learning is a type of machine learning that uses artificial neural networks to learn from large amounts of data and solve complex problems.
Security: Organizations need to take appropriate measures to ensure the security and privacy of their data in the cloud.
Data Governance: Organizations need to establish effective data governance policies and procedures to manage their data in the cloud.
Intra-Cloud Networks: These networks connect the virtual instances of an application to each other and cloud-provided services.
Persistent Storage: This storage is similar to traditional databases and file systems, but it is designed to be salable and reliable.
Integration: Organizations need to integrate their cloud analytics solutions with their existing IT systems and applications.
Data Privacy: Storing sensitive data on cloud servers raises concerns about maintaining the confidentiality and privacy of that information. Instances of unauthorized access, data breaches, or leaks could compromise private and sensitive information.
Data Integration: Power BI facilitates connectivity to an extensive array of data sources, encompassing cloud-based platforms like Azure Data Lake, Azure SQL Data Warehouse, and online services such as Excel, SharePoint, and more. This seamless integration process streamlines data aggregation and transformation, laying the groundwork for comprehensive analysis.
Data Modeling: Power BI equips users with a suite of tools for data modelling, encompassing functionalities such as relationship establishment, calculated column creation, and defining measures. This capability enables users to craft data models tailored to their specific business requirements and hierarchies, ensuring a structured data framework conducive to extracting meaningful insights.
Artificial Intelligence: Artificial intelligence (AI) is the ability of a machine to think and act like a human, including the ability to learn, reason, solve problems, and make decisions.
Wide-Area Delivery Networks (WANs): These networks deliver the application's content to end users from multiple data centers around the world.