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About 7,249 papers for “Data virtualization”

Data Virtualization

Scott Burk, David E. Sweenor, Gary Miner · Productivity Press eBooks · 2021

Cited by 0

Data virtualization

Kontaratos, Evangelos, Κονταράτος, Ευάγγελος · Athens University of Economics & Business · 2022

Modern times businesses environment is being characterized by the exponentially increasing availability of both structured and unstructured data. Organizations urgently need to utilize this data to make better-informed decisions. It is also estimated that by 2025, the volume of data circulated on the Internet will be around 163 zetabytes, 50% of them will be produced by various companies. Another interesting aspect of this data is that about 75% would be processed outside the stored infrastructure. This means that it is very likely that the format of the data in the infrastructure that is stored is not compatible with the formats that can be processed by the infrastructure of the organization that will exploit them. Data virtualization is a solution for such issues. Data virtualization software acts as a bridge across multiple, diverse data sources, bringing critical decision-making data together in one virtual place to fuel analytics. The main advantage of Data Virtualization is that it enables organizations to directly use the data they need, regardless of the source they come from or the format they have. In this paper we will present the concept of data virtualization and its general characteristics. The concept and characteristics of big data and their importance for modern organizations will also be presented. The main part of the study concerns the current situation in the implementation of the business approach.

Cited by 0Open access

Data Warehouse and Data Virtualization

Kanishka Mukherjee · Advances in medical technologies and clinical practice book series · 2015

This chapter contains as well as illustrates different innovations that is changing the way Clinical Drug Development and Safety organizations perceives IT and the ways and means through which these innovations are facilitating the change in business itself. The main content contains illustrations of two structurally different means to create data warehouses, the benefits of the approaches and the difficulties. It also explains the importance of data virtualization technology when implemented in the Clinical and Safety Organizations.

Cited by 0

ETL and data virtualization

Nishanth Reddy Mandala · World Journal of Advanced Research and Reviews · 2022

As organizations continue to adopt modern data architectures, the convergence of traditional ETL (Extract, Transform, Load) processes with data virtualization has emerged as a promising solution for integrating and managing complex data environments. Data virtualization allows real-time data access without physically moving the data, while ETL provides the foundation for data transformation and integration. This paper explores the integration of ETL and data virtualization, its benefits, and challenges, and provides insights into how businesses can leverage this approach to create a unified data management strategy.

Cited by 0Open access

Combating Dirty Data using Data Virtualization

Omkar Sawant · 2019

Data is the new Gold of the modern age. Organizational decisions, artificial intelligence, machine learning, time critical processes, analytics, etc. are driven by highly profiled data. Data gives an organization the ability to act in a corresponding situation and hence, keep up with the market. Financial returns are directly associated with the quality of data being used. Today's world has experienced a data explosion with the advent of data sources like Hadoop, data lakes, clusters and many more. Even more, the day to day devices that we use collect data to present meaningful statistics to us. There is an exponential increase in data volumes which are characterized by heterogeneous data formats and disparate data sources which in turn is complicating the task of providing uniform and profiled data to the business users for analysis. Warehousing is also not able to cope up with the on-demand real-time data access. Holistic view of the entire data is extremely difficult to achieve and takes a considerable amount of time which is slowing down an organization's decision making capabilities. The complexity of the current systems has increased due to the interconnected nature of widely distributed systems which share data. Dirty data i.e. data which is inaccurate, inconsistent, unclean or incomplete is spread all across. As all the data is unrequired garbage data, there is no point in developing your business intelligence over this data. Data Clarity used along with Data Virtualization provides an exceptional solution to these problems by handling the complex data workloads with respect to heterogeneous data and disparate data sources and provides a holistic view of the data to the business organization.

Cited by 0

Virtualization Services for Data Grids

Reagan Moore, Chaitan Baru · Grid Computing · 2003

This chapter contains sections titled: Introduction Digital Entities Data, Information, and Knowledge Information Integration Summary Acknowledgements References

Cited by 23

Data Virtualization and Digital Agility

Seth Earley · IT Professional · 2016

Organizations must increasingly deal with never-ending, complex, and costly requirements for using all of their data sources to produce value. Big data must be integrated with small data, but trying to bring varying data sources together without a set of structures and processes supported by the right technology leads to the proverbial "data swamp." One technique is to establish a master data management (MDM) program to coordinate different data sources, which goes hand in hand with data virtualization to ensure data consistency and accessibility.

Cited by 6

Improving Data Center Resource . . . WITH VIRTUALIZATION

Timothy Wood · 2009

The increasing demand for storage and computation has driven the growth of large data centers–the massive server farms that run many of today’s Internet and business applications. A data center can comprise many thousands of servers and can use as much energy as a small city. The massive amounts of computation power required to drive these systems results in many challenging and interesting distributed systems and resource management problems. In this thesis I investigate challenges related to data centers, with a particular emphasis on how new virtualization technologies can be used to simplify deployment, improve resource efficiency, and reduce the cost of reliability. I first study problems that relate the initial capacity planning required when deploying applications into a virtualized data center. I demonstrate how models iv of virtualization overheads can be utilized to accurately predict the resource needs of virtualized applications, allowing them to be smoothly transitioned into a data center. I next study how memory similarity can be used to guide placement when

Cited by 0

Big Data Virtualization in Edge Computing

Safa Ismail Nakade · International Journal of Science and Research (IJSR) · 2023

Big data virtualization in edge computing is the core focus of this study, aiming to address the challenges of managing and utilizing large volumes of data generated at the network edge.Leveraging virtualization techniques, the goal is to integrate and abstract heterogeneous data sources across the edge network, providing a unified and virtualized view of the data infrastructure.The research investigates the specific challenges, opportunities, and benefits of adopting big data virtualization in edge computing, exploring techniques and architectures for efficient data management and processing in distributed edge environments.Implementation considerations such as security, privacy, and performance are examined, drawing insights from an extensive review of literature and case studies.By advancing the understanding of state -of -the -art techniques and future research directions, this study seeks to enable efficient and scalable management of big data at the edge, unlocking the full potential of edge computing for data -intensive applications.

Cited by 0Open access

Data analysis of cloud virtualization experiments

Pedro R. X. do Carmo, Eduardo Freitas, Assis T. de Oliveira Filho et al. · arXiv (Cornell University) · 2026

The cloud computing paradigm underlines data center and telecommunication infrastructure design. Heavily leveraging virtualization, it slices hardware and software resources into smaller software units for greater flexibility of manipulation. Given the considerable benefits, several virtualization forms, with varying processing and communication overheads, emerged, including Full Virtualization and OS Virtualization. As a result, predicting packet throughput at the data plane turns out to be more challenging due to the additional virtualization overhead located at CPU, I/O, and network resources. This research presents a dataset of active network measurements data collected while varying various network parameters, including CPU affinity, frequency of echo packet injection, type of virtual network driver, use of CPU, I/O, or network load, and the number of concurrent VMs. The virtualization technologies used in the study include KVM, LXC, and Docker. The work examines their impact on a key network metric, namely, end-to-end latency. Also, it builds data models to evaluate the impact of a cloud computing environment on packet round-trip time. To explore data visualization, the dataset was submitted to pre-processing, correlation analysis, dimensionality reduction, and clustering. In addition, this paper provides a brief analysis of the dataset, demonstrating its use in developing machine learning-based systems for administrator decision-making.

Cited by 0Open access

Data Center Virtualization and Secure Data Storage Architecture

Yan Zhang · Lecture Notes in Education Arts Management and Social Science · 2025

With the rapid development of data center virtualization, secure data storage has become a crucial issue. This paper aims to explore and design an efficient secure data storage architecture to address the challenges of data security in virtualized environments. The article first analyzes the impact of virtualization technology on data storage security, including data leakage, tampering, and availability issues. Then, this paper proposes a comprehensive secure data storage architecture, which includes data encryption, access control, backup and recovery strategies, and audit and monitoring mechanisms. Through case studies and security performance evaluations, this paper verifies the effectiveness and feasibility of the proposed architecture. Finally, the paper summarizes the research findings and proposes suggestions for future research directions, to provide references for data center managers and policymakers.

Cited by 0Open access