Next-Gen Data Ecosystems: Domain-AI across Spark, ETL, and Batch Intelligence
Abstract
: Domain-AI has made dramatic advancements in its ability to handle the world's data via Artificial Intelligence and is now at a 10X enhancement in the number of inferencing which can be performed, an 89% decrease in Latency (Time needed) and a 35% reduction in the cost of its services to its clients like Walmart and JPMorgan Chase compared to the past. The key driver for this huge growth has been Domain-AI's implementation of an established production architecture which combines Machine Learning into query execution which has ultimately eliminated most of the typical inefficiencies associated with Data Management. Other enhancements include the addition of AI Native Operators to SPARK 4.0 and the integration of Vector data into a new database which improves Schema Inference and helps to resolve Data Drift issues without requiring the retraining of Models. The challenge remains to provide Real-Time Inference and new methods of Real-Time ETL processes utilizing Quantum Technology. The future of the industry is moving toward Cloud Lakehouses as the predicted standard architecture by 2027 and the goal of maximizing the value of Data to Organizations through improved technologies and methodologies that enhance the value of Data Intelligence will enable organization to receive high returns on their investment in data. It is imperative that organization begin implementing these technologies in order to position themselves to compete successfully in the future.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 9 No. 2 (2026): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
382-390 |
Published |
March 20, 2026 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Nagabhushanam Bheemisetty (%2026). Next-Gen Data Ecosystems: Domain-AI across Spark, ETL, and Batch Intelligence. International Journal of Science, Research and Technology , Vol. 9 No. 2 (2026): International Journal of Science, Research and Technology (IJSRAT) , pp. 382-390. https://doi.org/10.15662/IJSRAT.2023.0605009 |
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