Agreeable Data

And we’re live from #SnowflakeSummit! We’re incredibly excited to welcome thousands of attendees for a week filled with insights on data, apps, and AI 🥳️ Snowflake the bear is here, our Data Superheroes are suited up, and we’ve got plenty of fresh snow in San Francisco! Stay tuned for behind-the-scenes and live updates from Summit ❄️ There's no reason to miss out on the fun, register for our livestream: https://lnkd.in/gYVKkyr9

There has been a lot of attention on a cybersecurity incident involving Snowflake. We want everyone to have the latest and also separate truth from fiction. We recently observed and are investigating an increase in cyber threat activity targeting some of our customers’ accounts. There have been a number of news stories based on an inaccurate Hudson Rock blog post that has since been taken down, claiming Snowflake’s systems have been breached. In fact, our investigation to date shows Snowflake’s product has not been breached. We are working with outside cybersecurity companies to independently validate our findings. You can learn more and read our latest updates here:  https://lnkd.in/g-wynJSQ

Indulge your curiosity and get some practical advice from David McCandless this week: 🍦 Indulge your curiosity 🔹 How does one pivot from chemical engineering in oil & gas to data? 🔹 What’s it like working in data at Amazon? 🔹 Does the Amazon 6-page memo work? ✅ Get practical 🔹 How can you use time series forecasting to create business value? 🔹 What does an economical, but scalable, data stack look like for SMB? 🔹 What’s one key tradeoff to consider when communicating insights downstream? Listen: https://spoti.fi/3R6mlfw

I think the problem might be the pricing model? Like, when we first launched Mode, we charged different prices for creator and viewer seats. Back then, we figured that analysts and data engineers would be our power users, would value the product a lot. And we could charge a lot for those seats, and give away seats to "consumers" for free. But this doesn't really work. First, to make the economics work, you had to charge a relatively high price lot for analysts — roughly, a couple hundred dollars a month. Even if the total cost of an annual contract isn't that high, people balk at that sticker price; they're accustomed to SaaS products being tens of dollars a month, not hundreds. And second, the people who create stuff in data tools aren’t like designers or engineers, where there’s a pretty hard line between a creator and a consumer. Companies are full of people who want to occasionally write SQL queries or create their own reports. But they don’t want to do it as much as an analyst, and will argue that it’s not fair for them to pay the full analyst price for partial analyst usage. At Mode, first tried to solve this by negotiating a bunch of weird partial seat licenses, where customers would tell us they wanted 10 full seats and 50 partial seats, so we’d charge them for 20 and call it even. But this is really messy to sell and a huge pain for customers to manage. Plus, both of us were trying to triangulate around an agreeable annual price. All the partial seat antics were just gymnastics to make it work. So, we eventually threw it all out in favor of a flat per seat fee. But inevitably, this incentivized us to build more features for everyone, and one you do that, you're inevitably going to build a BI too. And so we did. Every other data product get stuck in the same trap, for what seems to be the same reason: It starts as a specialized tool; its pricing model encourages the company to build things that expand who can use the product; they build generic features like dashboards and drag-and-drop charts; they become BI. Show me your pricing page, I'll show you the outcome, I guess. https://lnkd.in/eeMDvYNU

BREAKING NEWS!!!!!! 😱 Snowflake has released the Public Preview of Notebooks!! The release notes are not available at this time! This is an incredibly powerful feature that hasn't been seen in a while. Here are some of its key capabilities: 1. Each notebook cell allows you to use either SQL or Python syntax, depending on your preference! 2. The cells within the notebook can be converted into tasks and scheduled for periodic execution! 3. Notebooks can be managed by integrating with Git! Use cases: 1. Python scripts for data ingestion can now be written as tasks within the notebook, eliminating the need for workflow tools like Airflow. 2. Machine learning models that utilize sensitive data can be created and experimented with entirely within Snowflake. What other use cases can you think of? ❄

Whoa - didn't see this coming. very interesting... 🤔 . I do love how apache iceberg is getting so much love right now!

Awesome chat with Nicolay Christopher Gerold about orchestration tools.

In my previous role as a CTO I always thought about investing in tools like making a hiring decision. I need to have a clear job to be done for the tool that is purchased and a plan of execution. Every dollar spent in hiring (a person or tool) needs to be put to work making the company better - or it is wasted spend. 🎙️ Matthew Kelliher-Gibson, MBA,Eric Dodds and I got to explore this very topic as it relates to data tools on the latest episode of The Data Stack Show. One of the highlights for me was when Matthew Kelliher-Gibson, MBA said: "Busyness is not business value. It's easy to think you're doing well because you're busy and meeting stakeholder demands. But that doesn't necessarily mean we're delivering what the business truly needs. We need to dig deeper and understand the real objectives." Its easy to get too caught up in the cycle of 'busy' without stopping to assess the real impact. How do we make sure that our tools are all actually pushing the business forward? 👇 I’d love to hear from you. How do you ensure your tool investments are truly adding value? #DataTooling #DataEngineering #DataScience ---------------------------- Follow The Data Stack Show on linkedin to stay up to date on the latest episodes!

Big News in the Data World! It looks like Snowflake Cortex is going to GA tomorrow (May 7th)! High Level: Leverage the power of summarization and generation to build your solutions and applications using Large Language Models (LLMs) from Snowflake, Mistral AI, Meta, Reka, and more in a governed manner. What Can You Do? - Improve customer experiences with accurate summaries and analysis of reviews, service tickets, chats, etc., without requiring prompt engineering. - Create semi-structured (e.g., JSON) summaries or generate custom content with Snowflake Arctic or any other LLM available in Cortex. - Turn documents into interactive Q&A chatbots with a truly integrated experience between LLMs and embed functions in Cortex with the chat elements of Streamlit. Exciting stuff! Tomorrow is a big day in Snowflake land.

The Massive Disconnect in Data Jobs: Is a Correction Coming? I believe there is a massive disconnect—and therefore a looming correction—in the data job market. The expectations and requirements of employers vs employees are at polar ends of the spectrum. Here's what I'm observing: Remote Data Job Postings: - 1000+ applicants within hours (mostly qualified people) In-person Data Job Postings (except maybe in tech hubs like NYC or Austin, TX): - 🦗 Little to no qualified applicants Interview Processes: - Far too many rounds of interviews - Often results in zero outside hires - "We didn't find the right candidate" or "We hired internally" New Position Approvals: - Very difficult to get approved - Some companies may be stalling, hoping AI can improve team efficiency and eliminate the need for the position The correction I hope to see: 1️⃣ Companies will realize the best talent can choose where to work—and will opt for organizations with the tools, policies, and culture that align with their needs. In my opinion a great data person may be 20x more valuable than an average one, outpacing even the 10x impact of top engineers. 2️⃣ Recognizing this, companies will improve their hiring processes—communicating more effectively and bringing in outside experts (recruiters, fractional data help, etc) when needed to maximize their for their desired outcome. 3️⃣ Organizations will adopt a growth mindset around AI, seeing it as a means to augment rather than replace data jobs. Is this perspective a little optimistic? Absolutely. But hey—it's only Monday. I'm curious to hear your thoughts on this disconnect in the data job market and the potential shifts on the horizon? Let me know in the comments - fair game for the pessimists and optimists alike! 😀