Advancing Polyglot Big Data Processing using the Hadoop ecosystem
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908328263680000 |
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| author | Seabra, Antony Lifschitz, Sergio |
| author_facet | Seabra, Antony Lifschitz, Sergio |
| contents | This article explores the utilization of the Hadoop ecosystem as a polyglot big data processing platform, focusing on the integration of diverse computation and storage technologies and their potential advantages in certain computational contexts. It delves into the potential of this ecosystem as a unified platform highlighting its architectural foundations and their complementary strengths in distributed storage, processing efficiency and real-time analytics. The article explores potential use cases within domains such as Smart Cities and Social Networks, illustrating how the platform's diverse components can be orchestrated in a polyglot manner and how these fields can benefit from the ecosystem's capabilities. Finally, the article concludes by showcasing alternatives for future research, including specialized architectural aspects of the ecosystem to advance the polyglot paradigm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14322 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing Polyglot Big Data Processing using the Hadoop ecosystem Seabra, Antony Lifschitz, Sergio Distributed, Parallel, and Cluster Computing This article explores the utilization of the Hadoop ecosystem as a polyglot big data processing platform, focusing on the integration of diverse computation and storage technologies and their potential advantages in certain computational contexts. It delves into the potential of this ecosystem as a unified platform highlighting its architectural foundations and their complementary strengths in distributed storage, processing efficiency and real-time analytics. The article explores potential use cases within domains such as Smart Cities and Social Networks, illustrating how the platform's diverse components can be orchestrated in a polyglot manner and how these fields can benefit from the ecosystem's capabilities. Finally, the article concludes by showcasing alternatives for future research, including specialized architectural aspects of the ecosystem to advance the polyglot paradigm. |
| title | Advancing Polyglot Big Data Processing using the Hadoop ecosystem |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.14322 |