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| Format: | Recurso digital |
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Zenodo
2023
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| Online Access: | https://doi.org/10.5281/zenodo.15096256 |
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| _version_ | 1866902131030622208 |
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| author | Raghavender Maddali |
| author_facet | Raghavender Maddali |
| contents | <p>The quick evolution of artificial intelligence (AI) has enabled the creation of autonomous AI agents that transform real-time data conversion and Extract, Transform, Load (ETL) automation. The AI agents utilize reinforcement learning and self-adaptive algorithms to automate data ingestion, cleansing, and integration, leading to tremendous improvements in efficiency, scalability, and decision-making in data-intensive environments. Automating ETL processes helps organizations cut operational expenses, reduce the role of humans, and provide accuracy to data. AI integration with ETL processes allows real-time learning and adaptability and provides flexibility to dynamic data environments. Automated ETL decision-making based on AI enhances data governance, security, and regulation. This essay discusses critical methodologies, concerns, and future directions of autonomous AI agents in real-time data processing, with emphasis on their revolutionary effect on businesses today.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15096256 |
| institution | Zenodo |
| language | |
| publishDate | 2023 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Autonomous AI Agents for Real-Time Data Transformation and ETL Automation Raghavender Maddali <p>The quick evolution of artificial intelligence (AI) has enabled the creation of autonomous AI agents that transform real-time data conversion and Extract, Transform, Load (ETL) automation. The AI agents utilize reinforcement learning and self-adaptive algorithms to automate data ingestion, cleansing, and integration, leading to tremendous improvements in efficiency, scalability, and decision-making in data-intensive environments. Automating ETL processes helps organizations cut operational expenses, reduce the role of humans, and provide accuracy to data. AI integration with ETL processes allows real-time learning and adaptability and provides flexibility to dynamic data environments. Automated ETL decision-making based on AI enhances data governance, security, and regulation. This essay discusses critical methodologies, concerns, and future directions of autonomous AI agents in real-time data processing, with emphasis on their revolutionary effect on businesses today.</p> |
| title | Autonomous AI Agents for Real-Time Data Transformation and ETL Automation |
| url | https://doi.org/10.5281/zenodo.15096256 |