DataLab as a Service: Distributed Computing Framework for Multi-Interactive Analysis Environments
During the current data era, data analysis across multiple disciplines has become a critical task for researchers to obtain meaningful insights and solve complex problems that are immeasurable using traditional technologies. Big Data has led to the development of state-of-the-art technologies that h...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10858125/ |
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| author | Aida Palacio Hoz Andres Heredia Canales Ezequiel Cimadevilla Alvarez Marta Obregon Ruiz Alvaro Lopez Garcia |
| author_facet | Aida Palacio Hoz Andres Heredia Canales Ezequiel Cimadevilla Alvarez Marta Obregon Ruiz Alvaro Lopez Garcia |
| author_sort | Aida Palacio Hoz |
| collection | DOAJ |
| description | During the current data era, data analysis across multiple disciplines has become a critical task for researchers to obtain meaningful insights and solve complex problems that are immeasurable using traditional technologies. Big Data has led to the development of state-of-the-art technologies that have revolutionized the process of experimentation. These innovations span from automating the setup of the infrastructure required for data analysis to providing user-friendly interfaces that simplify coding and result visualization. However, managing and scaling these resources for large-scale data processing remains a challenge. In this work, we introduce a novel framework called Datalab as a Service which integrates cutting-edge and open-source technologies to offer an online platform designed for both resource providers and researchers. The platform enables users to easily and automatically deploy interactive environments tailored for data analysis, thereby streamlining the process of managing computational resources. Through DLaaS, users gain access to cloud-based infrastructures and distributed computing resources, which are essential for performing compute-intensive tasks on massive datasets. The framework ensures scalability, resource management and optimization, and high availability, all within an accessible and user-friendly platform. Furthermore, this paper presents several use cases where researchers have successfully utilized DLaaS resources, demonstrating its practical applications in real-world scenarios. |
| format | Article |
| id | doaj-art-bcb2371c38ef48b4a0e6a985a792705a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-bcb2371c38ef48b4a0e6a985a792705a2025-02-07T00:01:20ZengIEEEIEEE Access2169-35362025-01-0113225662257710.1109/ACCESS.2025.353663710858125DataLab as a Service: Distributed Computing Framework for Multi-Interactive Analysis EnvironmentsAida Palacio Hoz0https://orcid.org/0000-0003-2289-0254Andres Heredia Canales1Ezequiel Cimadevilla Alvarez2https://orcid.org/0000-0002-8437-2068Marta Obregon Ruiz3Alvaro Lopez Garcia4https://orcid.org/0000-0002-0013-4602Instituto de Física de Cantabria (IFCA, CSIC-UC), Santander, SpainInstituto de Física de Cantabria (IFCA, CSIC-UC), Santander, SpainInstituto de Física de Cantabria (IFCA, CSIC-UC), Santander, SpainInstituto de Física de Cantabria (IFCA, CSIC-UC), Santander, SpainInstituto de Física de Cantabria (IFCA, CSIC-UC), Santander, SpainDuring the current data era, data analysis across multiple disciplines has become a critical task for researchers to obtain meaningful insights and solve complex problems that are immeasurable using traditional technologies. Big Data has led to the development of state-of-the-art technologies that have revolutionized the process of experimentation. These innovations span from automating the setup of the infrastructure required for data analysis to providing user-friendly interfaces that simplify coding and result visualization. However, managing and scaling these resources for large-scale data processing remains a challenge. In this work, we introduce a novel framework called Datalab as a Service which integrates cutting-edge and open-source technologies to offer an online platform designed for both resource providers and researchers. The platform enables users to easily and automatically deploy interactive environments tailored for data analysis, thereby streamlining the process of managing computational resources. Through DLaaS, users gain access to cloud-based infrastructures and distributed computing resources, which are essential for performing compute-intensive tasks on massive datasets. The framework ensures scalability, resource management and optimization, and high availability, all within an accessible and user-friendly platform. Furthermore, this paper presents several use cases where researchers have successfully utilized DLaaS resources, demonstrating its practical applications in real-world scenarios.https://ieeexplore.ieee.org/document/10858125/Distributed computingKubernetesinteractive environmentscloud computingJupyter notebooks |
| spellingShingle | Aida Palacio Hoz Andres Heredia Canales Ezequiel Cimadevilla Alvarez Marta Obregon Ruiz Alvaro Lopez Garcia DataLab as a Service: Distributed Computing Framework for Multi-Interactive Analysis Environments IEEE Access Distributed computing Kubernetes interactive environments cloud computing Jupyter notebooks |
| title | DataLab as a Service: Distributed Computing Framework for Multi-Interactive Analysis Environments |
| title_full | DataLab as a Service: Distributed Computing Framework for Multi-Interactive Analysis Environments |
| title_fullStr | DataLab as a Service: Distributed Computing Framework for Multi-Interactive Analysis Environments |
| title_full_unstemmed | DataLab as a Service: Distributed Computing Framework for Multi-Interactive Analysis Environments |
| title_short | DataLab as a Service: Distributed Computing Framework for Multi-Interactive Analysis Environments |
| title_sort | datalab as a service distributed computing framework for multi interactive analysis environments |
| topic | Distributed computing Kubernetes interactive environments cloud computing Jupyter notebooks |
| url | https://ieeexplore.ieee.org/document/10858125/ |
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