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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.14314 |
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| _version_ | 1866915251421708288 |
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| author | Seabra, Antony Lifschitz, Sergio |
| author_facet | Seabra, Antony Lifschitz, Sergio |
| contents | This article explores the use of the Hadoop-Spark ecosystem for social media data processing, adopting a polyglot approach with the integration of various computation and storage technologies, such as Hive, HBase and GraphX. We discuss specific tasks involved in processing social network data, such as calculating user influence, counting the most frequent terms in messages and identifying social relationships among users and groups. We conducted a series of empirical performance assessments, focusing on executing selected tasks and measuring their execution time within the Hadoop-Spark cluster. These insights offer a detailed quantitative analysis of the performance efficiency of the ecosystem tools. We conclude by highlighting the potential of the Hadoop-Spark ecosystem tools for advancing research in social networks and related fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14314 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Polyglot Data Processing in Social Networks using the Hadoop-Spark ecosystem Seabra, Antony Lifschitz, Sergio Distributed, Parallel, and Cluster Computing This article explores the use of the Hadoop-Spark ecosystem for social media data processing, adopting a polyglot approach with the integration of various computation and storage technologies, such as Hive, HBase and GraphX. We discuss specific tasks involved in processing social network data, such as calculating user influence, counting the most frequent terms in messages and identifying social relationships among users and groups. We conducted a series of empirical performance assessments, focusing on executing selected tasks and measuring their execution time within the Hadoop-Spark cluster. These insights offer a detailed quantitative analysis of the performance efficiency of the ecosystem tools. We conclude by highlighting the potential of the Hadoop-Spark ecosystem tools for advancing research in social networks and related fields. |
| title | Towards Polyglot Data Processing in Social Networks using the Hadoop-Spark ecosystem |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.14314 |