CORE
Senior Software Engineer at Yahoo
Jubin Soni is a Senior Software Engineer with 14+ years building scalable systems, real-time data pipelines, and AI platforms for technology and media companies. He is a Fellow of BCS, The Chartered Institute for IT, an IEEE Senior Member, and a Full Member of Sigma Xi, with research published in IEEE venues and a book with Manning Publications. He has spoken at P99 CONF and APIdays on distributed systems, serverless architectures, and applied ML.
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| Reputation: | 4955 |
| Pageviews: | 280.5K |
| Articles: | 63 |
| Comments: | 11 |
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
Oct 13, 2026 · Adam Bellemare
Really helpful follow up. The way you break down event design makes the tradeoffs much easier to reason about. How do you decide when an event should carry full state versus just an identifier?
Oct 13, 2026 · Ayodele Johnson
Great read. Bringing BI tooling straight into the pipeline makes release health so much easier to see for the whole team. Which open source BI tool has worked best for you in a real DevOps setup?
Oct 13, 2026 · Stefan Wolpers
Loved this take. Adding AI on top of messy processes really does just speed up the mess. What was the first thing your team fixed before bringing AI in?
Oct 13, 2026 · Akash Lomas
Super helpful breakdown of how a 30B model can actually run on a laptop. The quantization part was explained really well. What laptop specs would you suggest for a smooth experience?
Oct 09, 2026 · Akhil Madineni
Great write up. Keeping the research state durable when a worker dies is such a practical idea for real agents. Have you tried this with really long running research tasks yet?
Oct 09, 2026 · sunil paidi
Really enjoyed this one. The way you explained what changes once search hits real scale was easy to follow. Did you have to rethink your indexing approach much along the way?
Oct 08, 2026 · John Vester
Great point about the authoring bottleneck in test automation. Having an agent read real requirements and existing test libraries feels like a practical step forward.
Oct 06, 2026 · Otavio Santana
Nice walkthrough of how JNoSQL simplifies time-series work in Java. The InfluxDB examples make it easy to get started quickly.
May 20, 2026 · Jubin Soni, FBCS
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 Soni, FBCS
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.