Vaibhav N.

Big news: Snowflake Ventures is leading a strategic equity financing in AtScale. This investment validates the critical role of semantics in the modern enterprise data stack. As enterprises consolidate data on Snowflake AI Data Cloud, a unified, governed semantic layer is essential for consistent metrics and business logic. A semantic layer like AtScale makes accessing that data more accurate, accessible, and efficient by bridging the gap between business language and data language. AtScale’s Universal Semantic Layer provides this single source of semantic truth for customers like Fidelity Investments and The Home Depot. The company remains independent and interoperable, supporting a broad ecosystem of data platforms, BI tools, and AI applications. This is a good moment to reflect on the future of data and AI. New to semantic layers? I'll post a tutorial link below. And here are five big topics from the 2025 Semantic Layer Summit (link to Substack articles on these below): 1️⃣ Semantic layer standardization is coming 2️⃣ Semantic layers are BI’s Gift to AI 3️⃣ Semantic layers make AI 3X more accurate 4️⃣ Long live SQL (with semantic layers to help) 5️⃣ The great debate: Should semantics be in your database? This is a pivotal moment for the data industry, and I’m thrilled to be a part of it! https://lnkd.in/eVJ276Pp

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This section on how dbt Labs transitioned from a purely PLG motion to layering on enterprise is a fascinating one. Two instructive passages that I have bolded (below from First Round Review's path to PMF series featuring dbt Labs) TLDR: GTM comprises your ICP, channel, and message. When you transition from PLG / bottom up to Enterprise / top down motion, naturally your ICP and channel also changes, but your messaging / proposition needs to change too, e.g., the enterprise may be multiple personas and are buying assurance as much as solution. --- "Handy found product-market fit organically for dbt as an open-source tool mostly used by data practitioners and developers. But a few years into running a commercial business, he realized he had to build a growth curve all over again with C-suite data leaders. “Even though we had an unbelievable amount of market pull, as we initially commercialized, it wasn’t easy for us to transform this open-source command line tool into a product that enterprises would pay a million dollars for,” says Handy. “When you have enough product-market fit, sometimes it allows you to get away with not being super tight on product marketing or sales motions. So around 2022, we went from this gigantic acceleration curve and overnight we realized, we have to sit at the adults’ table and figure things out real fast,” says Handy. After the PLG flame started to fizzle, Handy turned his attention to layering on a sales-led motion for the cloud platform. “We had to focus our efforts on telling cohesive stories to senior data leaders. We had to have a very clear, explainable answer to the question, ‘Why should I use the commercial product and not the open-source product? And it had to be digestible by someone with a C in their title,” he says. Handy’s answer: dbt Cloud can handle complex data for companies of every size. “The longer people used dbt, the more complex their code became,” he says. “It was a problem for the most sophisticated dbt users, who were often at the largest companies. So there was a real opportunity for us to step in and solve that for them with dbt Cloud.” To tell that story to enterprise customers, Handy relied on data, naturally. “At a user conference we presented a chart that showed the number of dbt projects that had a certain number of models in them — over 100, over 1,000, et cetera,” he says. “We watched that number climb and we knew as users ourselves, ‘Oh my God, trying to work in a dbt project with 5,000 models in it is challenging.’ So we started with that quantitative data point and asked folks in our community about their experiences with these very large, complex dbt projects, and validated that this was a pain in the ass without a cloud platform.”

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𝗛𝗼𝘄 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿𝘀 𝗠𝗮𝗻𝗮𝗴𝗲 𝗦𝗲𝘀𝘀𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝘀 𝗮𝗻𝗱 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗪𝗶𝗻𝗱𝗼𝘄𝘀 Managing sessions in LLMs isn’t just about keeping track of prompts. It is about orchestrating memory, context, and tools seamlessly across multiple agents. That’s where MCP servers come in. Here’s the flow I captured visually → Context Window as Working Memory: Holds system prompt, user inputs, assistant responses, tool calls, and rolling context → MCP Servers: Each server maintains its own session state, ensuring consistency across interactions → System Prompt + Tools + Code Loops: Work together to power agentic behavior while grounding outputs in real tasks → LLM Integration: The context is constantly summarized and updated before being sent to the model This structure ensures scalable, multi-session orchestration, which is critical for enterprise AI where multiple tasks and tools interact simultaneously.

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Huge results from Bodo’s latest TPC-H SF1000 (~1TB) benchmark: Bodo DataFrames ran standard Pandas code 5× faster than PySpark and 122× faster than Dask on the same cluster hardware 🚀 Full breakdown below.

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🚀 Real-time analytics is no longer optional. It’s the foundation of modern data products. Thrilled to share our brand new StarRocks Introduction Video, narrated by our Developer Advocate Ron Kapoor 👇 🎥 https://lnkd.in/g-95bhwn In this short overview, you’ll learn how StarRocks is reshaping the real-time analytics landscape: ✨ Sub-second performance at scale, for internal analytics and customer-facing apps 🔗 One engine for your lakehouse, AI agents, BI tools, and streaming pipelines ⚡ Native vectorized compute + cost-based optimizer + hierarchical caching 🧠 Adaptive self-optimizing engine for dynamic materialized views and query planning 🌐 Works across shared-nothing and shared-data architectures ☁️ Seamless with AWS / GCP / Azure + S3 / Iceberg / Hudi / Delta Lake 🔥 Real-time ingestion from Kafka / Flink / Pulsar / CDC (Postgres, MySQL) In short: 💡 The speed of a warehouse + the flexibility of a lake = StarRocks Ready to build real-time dashboards, AI-driven applications, and interactive analytics at massive scale — without the complexity? 🔗 Watch the intro 🤝 Join our Slack community We’re excited to support teams who are building the next generation of data-driven products. #StarRocks #CelerData #RealTimeAnalytics #Lakehouse #OLAP

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It’s time to be real about what HTAP actually means and how to implement it in a way that supports agents and other similar workloads…

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Today I’m excited to introduce Wood Wide AI, founded by Pradeep Ravikumar and Varsha Raj, from our PearX S25 cohort! So many companies today are drowning in numeric data. Generative AI tools can’t make sense of it, but Wood Wide AI can. Pradeep and Varsha are building a table-native numeric AI that combines the flexibility of LLMs with the accuracy of ML and the reasoning power of neuro-symbolic AI to deliver real insights from any table, in any domain. Instead of relying on custom data science tooling and data teams, business users can directly run data analysis and predictions. The next evolution of tabular ML is here, and I’m excited to see what’s next for Wood Wide AI.

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A model launch is now a relay race. Infra, dev, apps pass the baton. New open models like MiniMax M2.7 now move through the stack much faster than before, flowing quickly across infrastructure, developer platforms, and app-layer distribution. This changes how model adoption works. What each layer does: - Infrastructure gets models deployed. (OpenRouter, Ollama) - Developer platforms make them usable. (Trae, opencode, kilocode) - Application layers turn them into products people actually touch. (Yupp, Zo) New models are arriving faster. But distribution itself is becoming a competitive advantage. New model launches propagate across all layers in days now. The best model may not win. The best-distributed one will. Link to a quick comparison in the comments.

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🗓️This Week at OpenAI 1️⃣ Expands enterprise distribution via $200M Snowflake partnership embedding GPT-5.2 into governed data stacks 2️⃣ Nears $20B NVIDIA investment at ~$830B valuation, reinforcing compute-backed capital alignment 3️⃣ Launches Frontier and adds ads to free ChatGPT, opening new enterprise and consumer monetization layers. 🚀🚀🚀 #OpenAI #OAI #Weekly

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https://lnkd.in/gwr_aAYk 🚀 Unlocking the Power of LLMs: It's All About the Ecosystem! Think of a Large Language Model (LLM) like a database: you send a query, you get a response. But unlike deterministic, structured databases, LLMs are stochastic, unstructured, and can sometimes hallucinate. The real magic? The ecosystem around the model that makes it reliable and impactful. Here’s a simple AI adoption strategy: treat the LLM as a black box that can produce brilliant responses or hallucinate. Control outcomes by: Pre-processing queries: Craft clear intent with cumulative context and guardrails (content moderation, jailbreak detection, sensitive data filtering). Post-processing responses: Evaluate and re-rank outputs for relevance, safety, and accuracy. Building a feedback loop: Refine prompts and learn from outputs to improve accuracy over time. The full AI interaction loop: User → Query → LLM → Response → Feedback → Next Query Each step is an opportunity to enhance safety, context, and quality. From human or AI agents wielding tools (memory, retrieval, orchestration) to red teaming and mechanistic interpretability, a robust LLM pipeline ensures reliable, responsible AI. How are you building your AI ecosystem? #AI #LLM #MachineLearning #PromptEngineering #ResponsibleAI #GenerativeAI #NLP #ContextEngineering

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I really enjoyed watching this interview with between Jon, Dheeraj and Vinod - there's a lot of very deep ideas in here. How to make work better with AI. How to make #ai #effortless by implementing the principles of #essentialism (see Greg McKeown's books). How this has the potential to disrupt our current off-the-shelf software packages. Very proud to see these ideas come to life every day at DevRev, and at our customers/partners.

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