Data Warehouse and Data Virtualization Comparative Study

Mousa, Ayad Hameed; Shiratuddin, Norshuhada

doi:10.1109/dese.2015.26

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…Recently, thanks to advantages such as high scaling capability and on-thefly processing [32], some research work emerged in the data virtualization domain, supporting ad-hoc queries on heterogeneous data stores. For example, Lawrence [25] proposed a generic standards-based architecture on top of both SQL and NoSQL systems, verified by MySQL and MongoDB.…”

Section: Data Integration and Virtualizationmentioning

confidence: 99%

Serving Hybrid-Cloud SQL Interactive Queries at Twitter

Tang

Wang²,

Wu³

et al. 2022

Software Architecture

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The demand for data analytics has been consistently increasing in the past years at Twitter. In order to fulfill the requirements and provide a highly scalable and available query experience, a large-scale in-house SQL system is heavily relied on. Recently, we evolved the SQL system into a hybrid-cloud SQL federation system, compliant with Twitter's Partly Cloudy strategy. The hybrid-cloud SQL federation system is capable of processing queries across Twitter's data centers and the public cloud, interacting with around 10PB of data per day. In this paper, the design of the hybrid-cloud SQL federation system is presented, which consists of query, cluster, and storage federations. We identify challenges in a modern SQL system and demonstrate how our system addresses them with some important design decisions. We also conduct qualitative examinations and summarize instructive lessons learned from the development and operation of such a SQL system.

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Section: Data Integration and Virtualizationmentioning

confidence: 99%

Serving Hybrid-Cloud SQL Interactive Queries at Twitter

Tang

Wang²,

Wu³

et al. 2022

Software Architecture

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“…Integration of data is the foundation of business intelligence systems, which are essential components of decision support systems. There are two primary types of data integration: physical data integration, which includes data warehouses, and virtual data integration, which includes data virtualization [10]. Data Warehouse and Business Intelligence systems are divided into two primary components as data-driven Decision Support Systems: data warehousing ("getting data in") and business intelligence ("getting data out") [11].…”

Section: Introductionmentioning

confidence: 99%

Modelling Data Warehousing and Business Intelligence Architecture for Non-profit Organization Based on Data Governances Framework

Paramita

2023

J. Appl. Data Sci.

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Information systems research for non-profit organizations is an opportunity to make a contribution to the field of information systems, the adoption of information systems in this field is relatively tedious and there are few studies that examine this area; consequently, there are several research gaps in the domain of non-profit organizations that need to be solved. This research will focus on the development of data warehouse architecture and business intelligence for non-profit organizations. In this study, the Soft Systems Methodology (SSM) technique will be employed to develop a data warehouse architecture and business intelligence. This research will interview twenty individuals to collect primary data, review organizational policy documents, and conduct an open-ended survey. The obtained data will then be qualitatively analyzed, resulting in the formation of rich picture diagrams, CATWOE analysis, and conceptual models, which will ultimately form a data warehouse architecture and business intelligence. This research has produced a microservices-enhanced data warehouse architecture and business intelligence for non-profit organizations.

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“…Data integration technologies (DT) are the technologies responsible for collecting, processing, and formatting data as input to decision support systems and similar systems [5], [6]. DT can be divided into two main categories, physical data integration, and the other category is virtual data integration [5], [7]. In the context of this study, we will use data virtualization technology to develop a CDSS.…”

Section: Introductionmentioning

confidence: 99%

Virtual data integration for a clinical decision support systems

Nasir,

Mousa,

Ali Alkhafaji

et al. 2023

IJECE

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<span lang="EN-US">Clinical decision support (CDS) supplies clinicians and their patients, and relevant staff with meaningful and timely information intelligently integrated or visualized to enhance health and the health sector. Data is the backbone of decision support systems, especially (clinical) ones. Data integration (either virtual or physical manner) is a powerful technique to manipulate a vast amount of heterogeneous data and prepare it as input for the decision-making process. The difficulties in manipulating data that have a physical data integration technique motivated the decision support developers to tend to data virtualization as a data integration technique. In this paper, a clinical decision support system was developed using the virtual data integration technique. The developed system was evaluated in terms of usability and its capability of providing clinical decision support. The evaluation findings indicate that the proposed system is highly usable and has a positive impact on supporting the clinical decision-making process.</span>

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Data Warehouse and Data Virtualization Comparative Study

Cited by 9 publications

References 8 publications

Serving Hybrid-Cloud SQL Interactive Queries at Twitter

Serving Hybrid-Cloud SQL Interactive Queries at Twitter

Modelling Data Warehousing and Business Intelligence Architecture for Non-profit Organization Based on Data Governances Framework

Virtual data integration for a clinical decision support systems

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