Saved in:
| Main Author: | |
|---|---|
| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2023
|
| Online Access: | https://doi.org/10.5281/zenodo.15096256 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of 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>