Junhua Wang

Excited to see real-world proof of ARM’s impact on streaming. This collaboration between Amazon Web Services (AWS), Playtech, and the Wowza team makes this a must-watch for anyone focused on streaming performance and cost.

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As more of you leverage foundational models in your organizations, remember they weren't trained or tuned on your internal metadata and taxonomies. Get a headstart with the metadata that naturally sits inside of Astronomer and is kept up-to-date automatically as your organization evolves. Companies leveraging orchestration metadata and lineage achieve higher quality answers with a fraction of the tokens. More to come!

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It’s been the headline of the week: 𝐈𝐁𝐌 𝐢𝐬 𝐚𝐜𝐪𝐮𝐢𝐫𝐢𝐧𝐠 𝐂𝐨𝐧𝐟𝐥𝐮𝐞𝐧𝐭. A big question on everyone’s mind is how this will reshape the streaming landscape. In Yingjun Wu’s words, this acquisition does not mark the end of anything! It only accelerates a shift that was already in motion. The whole data infra stack is moving upward, away from raw plumbing and toward products that deliver visible value. This shift shows up in three places: ▪️ How analytics vendors treat ingestion ▪️ How Apache Iceberg changes the role of Kafka ▪️ How Kafka itself has to evolve. For details, read: https://lnkd.in/gXUMRyPA #risingwave #streamprocessing #datainfra

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The AI Research Team at Cisco Central AI Org keeps innovating how LLMs can be applied to help customers in managing large complex operational problems. This blog that the team just published is a great example of addressing the 'Large Context Window' bottleneck, delivering a scalable framework that slashes LLM latency and operational costs without compromising precision. https://lnkd.in/giFG45Cx

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Most teams don’t fail at Iceberg because of the tech — they fail because of everything around it. You can have the best format, fastest engine, and cleanest tables… and still sink your Lakehouse if your tactics and strategy don’t align. On October 16, Yingjun Wu and I will talk about why Iceberg adoption can be tricky — and how not to mess it up. Expect real stories, tactical advice, and maybe one or two jokes about data teams turning schema evolution into a full-time job. 🧊 This isn’t just about config files — it’s about mindset, ownership, and the reality of running open data infra in production. 🎟️ Save your seat: https://luma.com/fga9usb8 #Lakehouse #Iceberg Apache Iceberg #DataEngineering Vakamo RisingWave

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Jensen Huang dropped a number at #GTC 2026 that stopped me mid-scroll. "I believe computing demand has increased by 1 million times over the last two years." Jensen Huang, GTC 2026 Not over a decade. Not someday. Two years. Then came the slide that reframed everything for me. The entire AI infrastructure thesis in one frame: ▸ AI Factories are the Industrial Infrastructure of the AI Era ▸ Inference is the Workload ▸ Tokens are the New Commodity ▸ Compute is Revenue And the line Jensen said out loud: "It's now a factory to generate tokens." on the All-In podcast at GTC, Jensen laid out a thought experiment. If a $500K/year developer spent less than $250K on tokens by year end, he'd be "deeply alarmed." My read on that: it's not a pricing benchmark. It's a provocation. It's Jensen saying we are still dramatically underestimating how much value a token can unlock. Here's where I land on all of this. The cost of generating tokens is dropping fast. The value of what those tokens can do is rising exponentially. The gap between those two curves? That's where the real opportunity lives. But and this is the part most people skip the gap only gets captured if the infrastructure beneath it is built right. #Tokens don't generate themselves. #Inference doesn't optimize itself. And enterprises won't run mission-critical agentic workloads on infrastructure that can't guarantee latency, throughput and cost-per-token at scale. Whoever builds that layer , reliably, efficiently and at enterprise grade #owns the #AIstack. The inference delivery platform. At NeevCloud®, this is exactly what we're building toward on our AI SuperCloud platform. Because Jensen is right the AI factory era is here. The question is who builds the pipes that make the factory run. What's your take is token cost the most underloved problem in enterprise AI right now? Drop your thoughts below 👇 Note: I didn't attend GTC in person. Insights are from the official NVIDIA GTC keynote on YouTube and public coverage. Screenshot credit: NVIDIA GTC 2026 official livestream. #NvidiaGTC #AIInference #TokenEconomy #AgenticAI #EnterpriseAI #AISuperCloud #NeevCloud

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People often ask me about the differences in architecture between Amazon Dynamo (the 2007 SOSP paper), DynamoDB (the AWS serverless NoSQL database), and Aurora DSQL (the AWS serverless SQL databases). I captured the key points on durability, high availability, programming model, and trade-offs in my blog: https://lnkd.in/gkMG_tVD TL;DR: Despite the shared name, Dynamo and DynamoDB have little in common architecturally, and make very different trade-off decisions.

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Martin Zinkevich, Ph.D., reveals top 10 (out of 43) machine learning Best Practices at Google (Read all 43 rules here: https://lnkd.in/gFP3ytmN): https://lnkd.in/g9uDh_wi

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Andrew Ng reported from Davos, that AI deployment for transformative impact will require workflow redesign. It is "theory of constraints" all over again (remember Eli Goldratt's "The Goal"?), we have to understand that real business benefits come only from working across silos, from analyzing flow bottlenecks, from understanding that optimizing non-bottlenecks creates waste. As Ng noted, letting a thousand (AI) flowers bloom has failed to lead to significant payoffs. Obviously! We in the silicon industry are no strangers to this. Workflows have been stagnant for decades. Technologies for high level specification, high level synthesis, and formal verification are not yet integrated into the full silicon development flow. They are often done piece-meal, and enthusiasts' pilots are dropped at the hint of a failure or delay. People say they want formal verification in their flow, but want it yesterday and do not have the time to invest in training engineers for its rigorous usage. Ashish Darbari, responding to a question on "if formal verification is so powerful then how come it is not widely used", noted that the problem is in the name - the actual task to be done is formal analysis while the bug detection is only a corollary, but the industry puts the cart before the horse. Based on my experience with industrial practice of high level specifications and formal verification, I tend to agree with his succinct comment. Jan Rabaey, in a DAC keynote speech, said that someday he expects to see a comic book series entitled "adventures in formal verification". AI deployment faces the same problem, if not actively worked on by business leaders. To be fair, deployments of all disruptive technologies face this mental barrier, we are so used to thinking in terms of the old flow (using the old technology) that thinking of a new flow literally means us having to begin again from first principles - few leaders have the courage and the time to do that. Goldratt mentioned that he himself had faced the same mindset problem when deploying the early ERP solutions and was surprised why he wasn't getting the expected large benefits, and he said it took him many years before he could identify the core problem and define solutions for it, which he named (rather academically) as "theory of constraints". AI leaders would do well to rework the conventional workflows in a holistic manner in order to get the real benefits from this new technology. As Henry Ford had said, "If you always do what you've always done, you'll always get what you've always got".

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