ZeOmega

Attending my first Data Engineering Summit 2026 was more than just a conference experience — it was a glimpse into the future of data in the AI age. The sessions, conversations, and interactions with technology experts gave me a whole new perspective on how modern data engineering is transforming with AI, automation, and intelligent analytics. It was inspiring to see how quickly the industry is evolving and the kind of innovation being built around data today. Along with the insightful sessions, the technical games, quizzes, and interactive activities made the experience even more engaging and memorable. Left the event with new ideas, meaningful connections, and even more excitement to continue growing in the world of data and AI. A special thanks to Jayasimha Manchenahally{ಜಯಸಿಂಹ} and Shashidhar Somashekar for giving me this wonderful opportunity and supporting my learning journey. #DES2026 #DataEngineering #AI #BigData #Analytics #Learning #FutureOfData #Techcommunity #Networking

Honored to have one of my predictions featured by Healthcare IT Today as part of the discussion on “Enabling More Effective Collaboration and Data Exchange Between Payers and Providers.” One of the biggest shifts happening in healthcare is the evolution of payer and provider platforms from isolated systems of record into connected coordination ecosystems. The real advancement is no longer just exchanging data, it’s synchronizing workflows, decision logic, and operational processes in real time to reduce administrative friction, accelerate approvals, and improve outcomes. As CMS interoperability and prior authorization mandates continue to evolve, the industry is moving toward actionable interoperability where trusted data drives coordinated decisions across the healthcare ecosystem. Excited to see the industry continuing to move in this direction. #HealthcareIT #Interoperability #PriorAuthorization #HealthTech #CareManagement #ValueBasedCare #HealthcareInnovation https://lnkd.in/g5p9xMBP

@GIDS

"𝐅𝐢𝐱𝐢𝐧𝐠 𝐢𝐭 𝐥𝐨𝐜𝐚𝐥𝐥𝐲 𝐰𝐚𝐬𝐧’𝐭 𝐞𝐧𝐨𝐮𝐠𝐡" Contributed to 𝐕𝐞𝐜𝐭𝐨𝐫𝐃𝐁𝐁𝐞𝐧𝐜𝐡 by fixing an issue I ran into while benchmarking Milvus vs pgvector. Key learning: I had already fixed this in my local setup and moved on. But the PR process made me realize — local fixes don’t scale, contributions do. What works in one environment can quietly block many others. That’s what pushed me to take it one step further. My first PR didn’t make it (someone else got there first), but that was part of the process. I stayed with it, picked another issue I had faced, and pushed it through. Open source changed how I look at problems — not just solving them, but making sure others don’t hit the same wall. Most of the bugs we fix never leave our machines. That’s the gap. Open source is where those fixes actually start compounding. One small PR today can save someone else hours tomorrow. Huge thanks to XuanYang for the detailed code review and guidance. PR: https://lnkd.in/gzspyGvf #OpenSource #FirstContribution #BackendEngineering #VectorDB #PerformanceEngineering #DeveloperJourney

Come ask me how I can help do the same for you.

Hi Friends Most organizations underestimate Power BI development—until they scale. With Microsoft Power BI, simple reports are never the problem. Complex requirements without proper data architecture are. And that’s where reality hits developers hard. What business sees: “Just build a dashboard with these KPIs.” What developers deal with: No proper database design or star schema Multiple tables with inconsistent definitions KPIs changing across departments No single source of truth for core metrics Data coming from spreadsheets, APIs, and legacy systems Business logic that exists only in people’s heads Performance issues only discovered after deployment At that point, Power BI is no longer a reporting tool. It becomes a reconstruction exercise. Because without a solid data architecture: every measure becomes custom logic, every visual becomes a workaround, and every change risks breaking something else. The hardest part is not building reports. It is creating order from systems that were never designed for analytics in the first place. That’s why experienced Power BI developers spend more time fixing structure than building visuals. And why “just make a report” is never just a report. #PowerBI #BusinessIntelligence #DataAnalytics #DataEngineering #DataModeling #DataVisualization #MicrosoftPowerBI #Analytics #DataScience #SQL #ETL #DataArchitecture #BIDeveloper #TechThoughts

Inspired to hear from BG Beth Behn, Commanding General for Army TACOM, on a panel for scaling additive manufacturing capability for national defense. SMRT Architects & Engineers is actively supporting clients in the defense industrial base and across the federal government to build the next wave of advanced manufacturing facilities. If you’re here today, reach out to Ben Halleck, P.E. or myself. #RAPIDTCT #AdditiveManufacturing #3DPrinting #RAPIDTCT2026

𝐌𝐢𝐥𝐯𝐮𝐬 𝐯𝐬 𝐩𝐠𝐯𝐞𝐜𝐭𝐨𝐫 — 𝐏𝐚𝐫𝐭 3 After testing both in real scenarios, one thing became very clear: There is no single “best” choice. At first glance, it feels like a simple comparison — performance vs simplicity. But once you go deeper, it becomes a 𝐬𝐲𝐬𝐭𝐞𝐦 𝐝𝐞𝐬𝐢𝐠𝐧 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧, not just a database choice. 𝐖𝐡𝐞𝐫𝐞 𝐌𝐢𝐥𝐯𝐮𝐬 𝐬𝐭𝐚𝐧𝐝𝐬 𝐨𝐮𝐭: • Built specifically for vector search → better performance at scale • Lower latency for large datasets • Native support for distributed setup • Works well with Kubernetes (Helm-based deployments) • Horizontal scalability + high availability (HA) built-in • Supports advanced indexing (IVF, HNSW, etc.) • Better suited for production-grade AI systems 𝐖𝐡𝐞𝐫𝐞 𝐩𝐠𝐯𝐞𝐜𝐭𝐨𝐫 𝐟𝐢𝐭𝐬 𝐛𝐞𝐭𝐭𝐞𝐫: • Runs inside PostgreSQL → no new infrastructure • Easier setup, faster adoption • Works well for smaller datasets or hybrid queries (SQL + vector) • Backup/restore is straightforward using existing PostgreSQL tools • Familiar ecosystem (auth, monitoring, extensions already available) But here’s what people don’t talk about enough 👇 When choosing between them, you’re also deciding: • How you handle high availability (HA) • Your deployment model (K8s vs simple DB setup) • Authentication & authorization strategy • TLS/security configurations • Backup & restore workflows • Monitoring & observability (Prometheus, Grafana, etc.) • Operational overhead for your team These are not “secondary concerns.” They directly impact reliability, cost, and developer productivity. 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐭𝐫𝐚𝐝𝐞-𝐨𝐟𝐟: • Milvus → Performance + scalability + operational complexity • pgvector → Simplicity + integration + limited scaling 𝐌𝐲 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲: We spend a lot of time choosing the “best model.” But in production, 𝐭𝐡𝐞 𝐬𝐮𝐫𝐫𝐨𝐮𝐧𝐝𝐢𝐧𝐠 𝐬𝐲𝐬𝐭𝐞𝐦 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐣𝐮𝐬𝐭 𝐚𝐬 𝐦𝐮𝐜𝐡 𝐚𝐬 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥 𝐢𝐭𝐬𝐞𝐥𝐟. That’s where real engineering decisions happen. #machinelearning #systemdesign #softwareengineering #vectorsearch #milvus #postgresql #backendengineering

Quick poll was anyone else up at sunrise solving Provider Data and Directory problems this morning? #Perspecta