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Friday, December 16, 2022
Cloud Usage Data. Cleansing, Aggregation, Summarization, Interpretability and Usability (#CMGnews) - my presentation
Igor Trubin began his engineering career in 1979 as an IBM/370 systems engineer. He earned a Ph.D. in Robotics from St. Petersburg Technical University in 1986 and spent 12 years there as a professor teaching CAD/CAM and Robotics. He has published and presented more than 60 technical papers and conference presentations in robotics, artificial intelligence, IT performance, capacity management, anomaly detection, and FinOps. After moving to the U.S. in 1999, Igor worked at Capital One, IBM, and SunTrust Bank in senior engineering, architecture, and management roles. His 2002 CMG paper on exception detection received a Best Paper Award. He later developed Perfomalist.com, based on his original methods for anomaly, change-point, and trend detection, and created the online course “Performance Anomaly Detection.” At Capital One, he led development of cloud capacity-management and FinOps solutions, including the award-winning OptiCloud application. He has served on the CMG Board of Directors since 2015. Now semi-retired, Igor focuses on research, writing, and consulting. His current work expands his concept of the “Area of Normal Functioning” (ANF) from technical systems to human, orga
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The article highlights the importance of cloud usage data in optimizing cloud infrastructure through effective data cleansing, aggregation, summarization, and interpretation. Accurate cloud usage analytics help organizations understand resource consumption, identify inefficiencies, optimize costs, and make informed scaling decisions across complex cloud environments. By transforming raw cloud metrics into meaningful insights, businesses can improve operational efficiency while supporting governance and capacity planning.
Cloud computing platforms generate massive volumes of operational data that must be processed and analyzed to monitor infrastructure performance, optimize workloads, and improve resource utilization. Understanding cloud architecture, monitoring, and data-driven optimization enables developers to build reliable and scalable enterprise solutions. Students interested in practical cloud implementations can explore Cloud Computing Projects, featuring real-world applications involving cloud infrastructure, virtualization, Kubernetes, distributed systems, and cloud-native architectures.
Large-scale cloud monitoring also relies on big data technologies to collect, process, aggregate, and analyze infrastructure metrics generated from distributed environments. Modern big data platforms enable organizations to transform massive operational datasets into actionable insights that support automation, predictive analytics, and business intelligence. Those looking to strengthen their expertise in large-scale data processing can explore Big Data Projects, showcasing practical implementations of Hadoop, Apache Spark, ETL pipelines, cloud analytics, and enterprise-scale data engineering.
Readers interested in exploring enterprise-scale data processing and analytics can also refer to 15 Big Data Projects for Final Year Students. The guide showcases practical implementations involving Hadoop, Apache Spark, ETL pipelines, cloud data processing, distributed computing, and real-time analytics, making it a valuable resource for students interested in building scalable big data and cloud-based solutions.
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