CORE
Senior Software Engineer at Yahoo
US
Joined May 2025
Jubin Soni is a Senior Software Engineer with 14+ years of experience building scalable systems, real-time data pipelines, and AI-driven platforms for industry leaders in technology and media. With deep expertise spanning cloud-native architectures, distributed systems, and applied machine learning, Jubin brings a rare combination of engineering depth and research breadth to every problem he tackles. He is a published researcher with work appearing in IEEE and other peer-reviewed venues, and a Manning Publications author. Jubin holds IEEE Senior Member status and has spoken at technical conferences including P99 CONF, ACM and APIdays, sharing his expertise in distributed systems, serverless architectures, and AI with engineering communities globally. He is passionate about pushing the boundaries of what scalable software can do — and sharing those insights with fellow engineers through writing, research, and open source.
Stats
| Reputation: | 2617 |
| Pageviews: | 101.2K |
| Articles: | 46 |
| Comments: | 3 |
Microservices
Cognitive Databases, Intelligent Data
No longer passive storage and query engines, databases are becoming active, intelligent participants in how modern systems interpret, connect, and act on data. As AI moves deeper into production and enterprises adopt generative and agentic architectures, the database layer is being reshaped to support semantic search, contextual retrieval, and real-time decision-making. Vector databases, semantic indexing, and AI-driven optimization are changing how developers work with both structured and unstructured data, while the line between transactional and analytical systems continues to fade under hybrid workload demands.This report examines these industry shifts in practical terms, exploring how relational, NoSQL, vector, and multi-model systems are coming together to support AI-native applications. Our research, guest thought leadership, and practitioner insights look at how teams are bringing vector search into production, updating architectures for AI workloads, and redesigning data pipelines around semantic and contextual intelligence.
Comments
May 20, 2026 · Jubin Abhishek Soni
Really appreciate that, Georgi, thanks for taking the time to read it!
Apr 14, 2026 · arvind toorpu
Great article! thanks for sharing
Apr 14, 2026 · Jubin Abhishek Soni
Thanks, appreciate it! Great question, AKS autoscaling behaves similarly to self-managed Kubernetes since it uses the same HPA + Cluster Autoscaler components. HPA reacts quickly to spikes, but node scaling still has some lag due to provisioning time. AKS mainly makes this more reliable and easier to manage, but for sharp bursts, you may still need buffer capacity or tools like KEDA.