Craig Wiley

Big announcement from dbt Labs & Microsoft— you can now run dbt jobs in Azure Data Factory. Looking forward to seeing what our joint customers do with the streamlined product experience. The integration, which builds on a longstanding collaboration with Microsoft, will allow users to build, test and orchestrate dbt transformations directly within Fabric. #letsgo

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AI readiness starts with data trust. That’s why I’m excited to announce the Qualytics + Databricks partnership. Together, we’re enabling enterprises to run proactive, automated data quality directly on the Databricks Data Intelligence Platform, securely, at scale, and without architectural overhead. This partnership is about helping organizations move from reactive data cleanup to proactive control, so that the data fueling your AI models and BI dashboards is built on trusted data. The Qualytics + Databricks integration is available today. Databricks customers can deploy in hours and immediately start generating automated rules, rich profiling metadata, and anomaly detection. 👉 Learn more here: https://lnkd.in/evE9_BSv

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re:Invent Day 1 is done and it was packed! My day began with a roundtable with some of our best partners talking about how we can better help customers migrate and modernize their AI workloads and use cases. Then Priya Arora and I presented on-stage on how Agentic AI is opening opportunities for partners, from ISVs and SIs, to startups. We shared data from our survey we did with BCG showing the trends in customer adoption of AI, focusing on the industries and use cases with the highest momentum. We also uncovered areas where customers need the most help and support as they roll out their agentic use cases. After that I sat down with 3 key partners, grabbed lunch with a few AWS peers I don't get to see often enough, and had a lot of ad hoc hallway meetings. (It's hard to walk 10 feet at re:Invent without seeing someone you know) But my highlight was launching the new AI Competency and Agentic AI categories for partners. I had a chance to join the AWS OnAir team to talk about it. My team was the driving force behind the launch and I'm proud of what this means for our partner community. We spent months listening to partners tell us they needed a way to stand out in customer conversations around agentic AI. The new AI Competency creates three distinct specialization paths. Agentic AI Applications recognizes partners delivering production-ready autonomous systems. Agentic AI Tools validates partners providing the infrastructure and tooling that makes agent development possible. Agentic AI Consulting Services distinguishes partners with proven expertise helping enterprises design, build, and scale agentic deployments. Each path requires demonstrated technical depth and validated customer outcomes, not just certifications or marketing claims. We launched with 60 partners who achieved the AI specialization. That's the highest number of launch partners in any AWS Competency program so far. Partners like Loka, Anthropic, LangChain, and Mission are already proving their ability to deploy AI systems that handle real business processes autonomously. That validation matters when enterprises are making critical technology decisions. Partners achieving these specializations gain access to funding, co-marketing resources, and priority placement in customer engagements. But what matters most is the market differentiation. When customers are evaluating dozens of partners claiming agentic AI expertise, this competency provides a clear signal about who has actually done the work. For enterprises evaluating partners, this competency provides the differentiation signal you need. These partners have solved the hard problems of moving agents from demo to production, from single use case to enterprise platform. The timing matters. Enterprises are moving past whether to deploy agentic AI and into how to do it at scale. Partners with proven expertise become increasingly valuable as deployment complexity increases. That's what makes this competency so important.

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I remembered, 15 years ago, I asked an exec at Google "where does your BigData department sit?". The response from the Googler was: "there's no BigData unit. Here, everyone is BigData". (yes we used to call it BigData, not AI) I think that's happening now with all companies when it comes to AI department. There shouldnt be/wont be an AI unit; and every manager, from marketing to HR, will be expected to use AI deeply, to vibe code, etc. Every role will be both a management and a technical role. As AI agents turn execution into a commodity, humans need to add value by being orchestrators. There will still be teams, but there will be more individual contributors (ICs). The upside will be more agile execution, esp in test and learn. No more waiting for developer cycles as I used to during my Tencent days! Future workdays will be a hybrid of high-stakes human interaction and digital fleet management. What skills will remain key in this new future org architecture? 🧠 Precision of Thought: The humanities are the new computer science. AI is powerful, but only if you direct them in the right way! Logic and rhetoric are now your primary coding languages. 🤝 Human Trust: While AI might take up a lot of the tasks and decisions, the ability to connect deeply with another person, not just internally but also with external counterparties, will always be of value and cannot be automated. Technical skills are no longer a department. They are the baseline for professional existence. If you havent installed OpenClaw, Manus AI or Tencent's QClaw, it's about time to do so! Even if you found out "it is not that useful to you now", you would have learnt what it's all about; and it will pay dividend in future for sure. #AI #AIAgents #VibeCoding #FutureOfWork #Management Animoca Brands Animoca Minds Artificial Intelligence Association of Hong Kong

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Modern data strategy isn’t one-size-fits-all. Different architectures serve different purposes, and many organizations use a combination to balance scalability, governance, and analytics needs.

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Rely on MLflow beyond Domino—without losing governance. Our new Domino Blueprints article by Sameer Wadkar shows how to use Domino’s Experiment Manager (MLflow) from external clients for secure experiment tracking, CI/CD, and deployments to cloud, on-prem, or edge—while preserving project-level access control and auditability. Learn the supported approach (tokens, run context, headers) and get code to implement it fast. Read the Blueprint + repo: https://gag.gl/Ro8nQa

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When the shovel seller is worth more than the gold mine. ⛏️ Databricks just proved that in the AI gold rush, the plumbing is the most valuable asset. $5B raised. $134B valuation. The market is saying: Data is the new oil, and Databricks is the pipeline. This is a masterclass in market positioning. They transcended the “SaaS” label and became “AI Infrastructure.” You need to do the same for your business. 1️⃣ $134B is the ultimate validation of the data lakehouse model. 2️⃣ 55% growth is the proof the enterprise needs. 3️⃣ Irony, The “AI Bubble” is making a few people very rich. 4️⃣ Databricks is now a top-tier market leader. 5️⃣ Your data strategy just got a $134B blueprint. Stop chasing the shiny AI object and start building the data foundation that powers it. 👉 Tools to act now: News analysis on steroids This is my unfair advantage: ⏳ https://lnkd.in/daEhWsh6 💬 Comment below with your take ♻️ Repost if this resonates ➕ Follow Christian Schmidt for AI & business strategy ━━━━━━━ #Business #DataStrategy #AI #Databricks #Valuation 📎 Source: https://lnkd.in/d9--n68r

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Teradata up 33% in a day. Yes, they beat top and bottom line (congrats Teradata leadeship). This goes beyond an earnings pop. This was the market recognizing operating discipline, recurring durability, and real cloud traction after years of doubt. But here’s what actually matters to data and AI leaders: 1. New operating discipline and not just an AI hype stock pop. Recurring revenue strength and cash generation mean the platform stock pop isn’t being propped up by story alone. Importantly, 15%+ cloud ARR growth to 46% of total ARR shows traction and a clearer AI + hybrid positioning. 2. Hybrid is quietly back in the conversation. While everyone debates Snowflake vs. Databricks, we see Teradata’s hybrid positioning resonating again, especially in regulated, data-sovereign industries where “cloud only” isn’t practical. 3. AI economics require trusted throughput. Agents don’t sleep. Autonomous workloads can drive 10 to even 50x the number of queries. Platforms like Teradata, built to scale safely, with governance and policy controls by default, are suddenly looking more relevant. 4. The real test: delivering analytics → execution → autonomy. Financial analysts see credible momentum today, but Teradata must translate enterprise trust and hybrid strength into the shift from analytics -> automated execution -> autonomous systems. MyPOV The AI cycle has entered its accountability phase. Data gravity, trust, and throughput now matter as much as the latest LLM release. If Teradata can convert enterprise credibility (and focus on analytics) into execution-level AI to go beyond insights to governed outcomes, the rerating of Teradata's stock has room to run up. To read more, check out Larry Dignan's write-up on the announcement: https://lnkd.in/gCYSaePP #DataStrategy #CDAO #CIO #AIExecution #EnterpriseAI #Teradata #HybridCloud

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