Real-time Analytics in Cloud-based Data Solutions

IAEME PUBLICATION, 2024

Real-time data processing has emerged as a transformative force across industries, with global data volumes projected to reach 180 zettabytes by 2025. This technical analysis examines four significant implementations: PayPal's fraud detection system, Amazon's recommendation engine, Philips Healthcare's patient monitoring platform, and GE's predictive maintenance system. The article demonstrates how modern organizations leverage advanced technologies including edge computing, machine learning, and distributed systems to process millions of events per second while maintaining sub-100ms latencies.

IAEME PUBLICATION, 2025

Data engineering has become a critical enabler of real-time analytics and decision-making in modern cloud-native environments. The increasing diversity and volume of data from heterogeneous sources require efficient data processing pipelines to ensure low-latency insights. This paper explores the foundational aspects of data engineering, including data integration, pipeline orchestration, and real-time data processing techniques. It also examines challenges such as data consistency, scalability, and security in cloud-native architectures. Through an extensive literature review, the paper provides insights into past developments in real-time analytics and outlines best practices for designing robust data engineering solutions. The findings underscore the significance of cloud-based data frameworks and stream processing platforms in supporting agile decision-making.

SSRN Electronic Journal, 2012

Machine learning (ML) techniques are becoming commonplace in business and research alike. With the automatization of data collection efforts, evermore data is being captured, rendering the task of extracting insightful patterns increasingly challenging. In addition to this "data avalanche" becoming evermore overwhelming, the usage of more computationally intensive algorithms in predictive analysis tasks also gives rise to new issues and challenges, so that a ML approach typically entails a trade off between computational efficiency and predictive performance. In recent years, however, new paradigms in analytics have been proposed geared towards solving these data and computational challenges, including cloud computing, distributed computing, and parallel computing approaches. We set out to discern one of these new hypes in analytics, cloud computing, and present a case study hereof which was performed at KU Leuven. In this study, we set up a benchmarking experiment using the Microsoft Windows Azure cloud platform with Techila Technologies middleware, and compare the results with those obtained in a non-parallelized setup. The results show that significant analysis speed-ups can be gained when performing computational tasks in the cloud.

Data Science Foundation: White Paper Series, 2020

In the early days of the Data Analytics (DA) companies would sort data, assemble the data sets and establish the respective requirements prior to carrying on with the analysis. No wonder that by the time the analytics activities have been completed and the reports generated, the findings would often turn out to be outdated and could no longer be used for activities other than historic analysis. As the main purpose of employing DA in a commercial environment is performance improvement/optimization rather than making sense of historic studies, once the Real-Time Data (RTD) Analysis tools and patterns emerged, they were immediately embraced by the DA practitioners. While practical benefits of the Real-time DA over historic data analytics are obvious, it is also essential to understand and cater for its risks, challenges and limitations.

IEEE Access

With the advancement in intelligent devices, social media, and the Internet of Things, staggering amounts of new data are being generated, and the pace is continuously accelerating. Real-time analytics (RTA) has emerged as a distinct branch of big data analytics focusing on the velocity aspect of big data, in which data is prepared, processed, and analyzed as it arrives, intending to generate insights and create business value in near real-time. The objective of this paper is to provide an overview of key concepts and architectural approaches for designing RTA solutions, including the relevant infrastructure, processing, and analytics platforms, as well as analytics techniques and tools with the most up-to-date machine learning and artificial intelligence considerations, and position these in the context of the most prominent platforms and analytics techniques. The paper develops a logical analytics stack to support the description of key functionality and relationships between relevant components in RTA solutions based on a thorough literature review and industrial practice. This provides practitioners with guidance in selecting the most appropriate solutions for their RTA problems, including the application of emerging AI technologies in this context. The paper discusses the complex event processing technology that has influenced many recent data streaming solutions in the analytics stack and highlights the integration of machine learning and artificial intelligence into RTA solutions. Some real-life application scenarios in the finance and health domains are presented, including several of the authors' earlier contributions, to demonstrate the utilization of the techniques and technologies discussed in this paper. Future research directions and remaining challenges are discussed. INDEX TERMS Real-time analytics, data streaming, big data analytics, complex event processing, machine learning.

African Journal of Artificial Intelligence and Sustainable Development, 2021

In the era of big data, the efficient management and analysis of data have become paramount for businesses seeking to gain competitive advantages. Traditional data warehousing and ETL (Extract, Transform, Load) processes are increasingly challenged by the volume, velocity, and variety of data. To address these challenges, the integration of cloud-native Robotic Process Automation (RPA) and Artificial Intelligence (AI) presents a promising approach to architecting intelligent data pipelines. This research explores the design and implementation of such intelligent pipelines, emphasizing how they leverage cloud-native RPA and AI technologies to automate data warehousing processes and advance analytics capabilities. The study begins by analyzing the core components and architectural considerations for building intelligent data pipelines. Central to this architecture is the application of cloudnative RPA, which automates repetitive and time-consuming tasks within the ETL framework. RPA's ability to interact with disparate data sources and perform routine data handling tasks without manual intervention streamlines the ETL process, reduces operational costs, and minimizes human error. Additionally, RPA's scalability in cloud environments enables organizations to handle large-scale data operations efficiently. Complementing RPA, AI technologies play a critical role in enhancing data quality and enabling advanced analytics. AI-driven tools, such as machine learning algorithms and natural language processing models, are employed to transform raw data into actionable insights. These AI technologies support advanced data cleaning, anomaly detection, and pattern recognition, thereby improving the accuracy and reliability of the data warehouse. Real-time analytics capabilities are also significantly enhanced through AI, facilitating prompt and informed decision-making in dynamic business environments.

International Journal of Management Education for Sustainable Development, 2021

In today's data-driven world, organizations are increasingly relying on real-time data analytics to enhance decision-making processes. This paper explores the integration of artificial intelligence (AI) with stream processing technologies to facilitate dynamic decision support. By leveraging advanced algorithms and machine learning techniques, organizations can analyze data streams in real time, gaining actionable insights and improving operational efficiency. We discuss the challenges and opportunities associated with implementing AI-driven stream processing systems, as well as case studies that demonstrate their effectiveness in various industries. The findings indicate that such systems not only improve response times but also foster a proactive approach to decision-making in dynamic environments.

2017

Due to the growth of data volumes, volatility and variety, business analytics (BA) become an essential driver of today’s business strategies. However, BA is mainly adopted by large enterprises because it may require a complex and costly infrastructure. As many companies strive to make better use of their data and to adopt data-driven management paradigms, cloud computing has been discussed as a costeffective approach to BA implementation challenges. To date, there has been little attention on the emerging class of analytical cloud services, “Analytics as a service” (AaaS). This article aims at demarcating AaaS as a cloud offering through an explorative research approach based on multiple case studies. Based on the analysis of 28 AaaS offerings, we derive a classification scheme for AaaS business model configurations and derive five business model archetypes. We discuss cloud computing’s implications on the business analytics ecosystem where partner networks play an important role at...

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Big Data and Cloud Computing as two mainstream technologies, are at the center of concern in the IT field. Cloud Computing refers to the processing of anything, including Big Data Analytics, on the "cloud". The "cloud" is just a set of highpowered servers from one of many providers. They can often view and query large data sets much more quickly than a standard computer could. Essentially, "Big Data" refers to the large sets of data collected, while "Cloud Computing" refers to the mechanism that remotely takes this data in and performs any operations specified on that data. Cloud Computing services largely exist because of Big Data. Likewise, the only reason that we collect Big Data is because we have services that are capable of taking it in and deciphering it, often in a matter of seconds. The two are a perfect match, since neither would exist without the other. The combination of both yields beneficial outcome for the organizations. Not to mention, both the technologies are in the stage of evolution but their combination leverages scalable and cost-effective solution in big data analytics. Big data and Cloud computing are perfect combination. Besides that, there are also some real-time challenges to deal with. In this paper, discribes both the aspects. This paper introduces the characteristics, trends and challenges of big data. In addition to that, it investigates the benefits and the risks that may rise out of the integration between big data and cloud computing.

Cloud computing revolutionize IT and business by offering computing as a utility over the internet. The evolution from internet to a cloud computing platform, the emerging development paradigm and technology and how these will change the way enterprise applications should be architected for cloud deployment plays an important role but these enterprise technologies are critical to cloud computing. New cloud analytics and business intelligence (BI) services can help businesses (organizations) better manage big data and cloud applications.Analysing and gathering business intelligence (BI) has never been easy, but today BI is complicated further by overwhelming amounts of data loads and the number of data entry and access points. New cloud analytics advancements may offer BI relief and even profit-increasing predictability for enterprises. These new cloud analytics applications can deliver functional capabilities that can be easily, quickly and economically deployed, producing tangible and measurable benefits far more rapidly than in the past. Many organizations that recognized, effectively analysing their business needs and providing the data they require to make the right business decisions depends on a combination of internally generated data and externally available data.