Ryan A. Ekins

How does an organization benefit from Strategic Data Modeling? 💥 Consensus and visibility of the broader business model 💥More productive conversations with business teams 💥Better quality of requirements 💥Higher signal, lower noise in technical conversations 💥Cross-platform, cross-domain, and widely understood conventions 💥Big-picture visual overview of the business and its database footprint 💥Preliminary designs become implementation blueprints 💥Accelerating onboarding of new team members 💥Making data more accessible and unlocking self-service within organizations 💥Keeping the database landscape manageable at scale 💥Getting a handle on complex data pipelines Check out Serge Gershkovich's book, 𝐷𝑎𝑡𝑎 𝑀𝑜𝑑𝑒𝑙𝑖𝑛𝑔 𝑤𝑖𝑡ℎ 𝑆𝑛𝑜𝑤𝑓𝑙𝑎𝑘𝑒, to learn more! #DataModeling #SqlDBM #Cloud #Snowflake https://lnkd.in/gsQsJ_yb

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Announcing Databricks AI/BI, a new type of BI product that deeply understands your data’s semantics and democratizes analytics. Built on a compound AI system that powers: - AI/BI Dashboards: AI-powered, low-code solution with all BI capabilities you'd expert out of the box, for answering a fixed set of business questions. - Genie: a conversational interface that can learn your underlying data continuously based on human feedback, and flexibly answer questions. Learn more about AI/BI's design and what this means for self-service BI.

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11 years ago, Chris Degnan joined Snowflake as their first sales rep. Back then, they had no customers and no revenue. Today, Chris Degnan is Snowflake's CRO. And the company is an absolute juggernaut: - $2.81B in revenue, growing at 35.86% YOY. - A global team of over 7,000 employees, including 2,500+ sales reps. Here's some lessons we can take from Snowflake on how to build the foundation of a scalable GTM function: 1) Hire the right Head of Sales and give them latitude to discover your market In the early stages, a head of sales shouldn't focus on revenue. Their goal is to identify the Ideal Customer Profile (ICP). During his first year, Chris aimed for 8 customer meetings a week. Initially targeting the largest companies, he soon realized they were not early adopters. After several discussions, he pivoted to focus on cloud-friendly companies with native apps. This fine-tuned Snowflake’s approach to targeting the right customers. 2) Integrate customer feedback directly into the development process Chris's initial outreach was designed to collect market feedback. Every Friday, he compiled insights from that week’s customer meetings and shared them with the entire company. The CEO and engineering team leveraged this data to guide product development. This strategy was pivotal in achieving product-market fit before advancing their market strategy. 3) Educate customers before advancing to the purchasing stage Snowflake’s sales team embraced an "adopt before buying" model to ensure customers recognized the product’s value. Chris’s approach was straightforward: "Try it out. If you like and use it, then pay for it." This method mitigates the risk of overselling, which can lead to high churn rates—a common challenge from seed to Series A. 4) Scale the team based on data-driven insights Snowflake’s sales team was guided by demand signals. Chris’s first three hires were: 1 intern to help him build demand generation. 2 sales reps to manage the emerging demand. Initially, he divided the country into three regions, each managed by one of these team members. As specific regions showed increased demand, he scaled the team accordingly. What’s your favorite insight from Snowflake’s legendary growth? Let me know in the comments! P.S. Need help hiring GTM talent for Data, AI/ML, Compute, and Cloud Infra? DM me. We've assisted successful early-stage startups (backed by Zoom, Norwest, and Two Sigma Ventures) in finding A+ hires. #GTMstrategy #GTMteam #recruitment #sales #strategy 🗣️ Credit to GTMnow & Stage 2 Capital for their insightful GTM podcast with Chris Degnan. Highly recommended!

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Super interesting to see the launch of Snowflake Arctic for enterprise customers, will be interesting to see how customers benefit in Performance to Cost of models vs likes of DBRX ❄️❄️❄️

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Data waterfalls are incredibly powerful. Big companies have used them forever. B2C has built industries out of data waterfalls. Just go check out Snowflake and Databricks marketplaces. But, but... For the average outbound rep with a set account list - don't waste your time. All data goes through a waterfall. Sit on a data company presentation and you'll hear the term waterfall when they explain why the data is so good. And the benefits for an individual rep are so marginal. The secret really is in finding that special partner, industry, or unstructured data. What does it mean? Get the data that has the best quality for the record that's most important to you or hardest to get. Emails are everywhere and validation tools are free. Good mobile! 📱 And, and make sure you can afford to use it. That the data supplier's business model supports usage (cost-effective). Because pretty much all waterfall tools have a learning curve and some can get very expensive very fast. If you're an IC rep or an SDR, don't feel like you're missing out if you're not diving into a waterfall. Your data's already been through it. Waterfalls tools are incredibly powerful but if you're a rep. Don't go chasing waterfalls. #deepcut #sales #AE #sdr #outbound Mavlonbek Muratov

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“The future of Tableau is all about delivering on where we came from with the analyst, this is the deep visualizations, data cataloging, data prep, etc.,” Ryan Aytay said. “But it’s also about bringing data and AI for everyone, every user like myself, because I’m of course a business user, but I need data to do my job.” Ryan Aytay, Tableau CEO, shares his vision for the future of Tableau and how the business will continue to stay true to it’s core while disrupting the #AI and #analytics space. More on this and what announcements were made at Tableau Conference 2024 in VentureBeat.

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📓 Quick Read: In this post, Jon Alkan provides an overview of 3 Snowflake performance features: (1) Search Optimization Service, (2) Query Acceleration Service, and (3) Cache.

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There are rumors coming out of Snowflake Summit this week that Snowflake’s launching a semantic layer. Standardizing key metrics like revenue is crucial for streamlining analytics and reporting. DBT and Snowflake's semantic layer products will make this concept mainstream. But metrics aren't the only thing companies lack a standard definition for. Companies care about customers (hopefully) but often don't have a consistent customer definition. Consider a B2C company with customer interactions across multiple channels: 1. Anonymous (guest) visits from multiple devices 2. Logged-in users 3. Physical store interactions Are these interactions considered as one customer or multiple? Is there alignment on this definition between different teams? Is product measuring the funnels for the same set of customers that marketing is targeting with email campaigns? Is data science building life-time-value or churn models for the same customers who are receiving marketing emails? Without a consistent customer definition, teams can't have a shared understanding of the customer's journey. This results in a disconnected experience across product and marketing channels. There's a pressing need for a customer 360 semantic layer. That's what we're building with RudderStack Profiles.

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If you haven’t seen TX with SqlDBM yet, todays your chance! Our last session is at 10 am, message me and I’ll save a headset!🚀 #SqlDBM #DataModeling #SnowflakeSummit #Summit #snowflake #dataengineering

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If you are ingesting data from Kafka/Kinesis into Snowflake you should watch this short video by Charles Tan and Shawn Gordon on how you can use DeltaStream to shift left your workloads to reduce cost and latency! #Flink #Kafka #Snowflake #ShiftLeft https://lnkd.in/gnZ45MTa

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LinkedIn | Tealium #Tealium + Snowflake = better together! 🤝 Check out the #Snowflake Snowpipe Streaming API 👉 https://lnkd.in/gweTjPFv. Read the detailed outline how Snowflake & Tealium CDP complement each other here https://lnkd.in/gkXXHvaX.

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Azure Databricks and Power BI create a powerful data duo! Explore how automatic aggregations can optimise performance, reduce latency, and deliver faster insights for real-time decision-making. #AzureDatabricks #PowerBI #AutomaticAggregations

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Snowflake releases arctic-embed models, a family of 5 text embedding models that range from 23 to 334 million parameters. The models are the result of search knowledge, and research and development that Snowflake acquired last May via Neeva. 💵 The models outperforms retrieval performance, having one-quarter of the estimated parameters and one-third of the dimensions compared to Open AI. 📊 https://lnkd.in/gjJy9Yte Some more insights on how the technical achievements are detailed in this blog 🦾https://lnkd.in/g_aYgwrH

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It's no secret that Databricks default cluster settings aren't always the best for performance. Here's a real user example of when optimized EBS settings got 55% cost and runtime reduction - and even got their runtimes to meet their 2 hr SLA! Gradient custom tunes these settings for each job to achieve optimal performance while maintaining SLAs - going far beyond default settings. Best of all, this happened automatically across thousands of jobs while engineers were busy working on more important tasks. Global heuristics and default settings are designed for the best "general" settings. However, true gains come from customized optimizations for each pipeline, no two are alike. In my opinion, this is the true power of AI - customization at scale. It's a complete game changer when it comes to cloud infrastructure! Give Gradient a shot yourself today to see how automation can save your engineers time and money. Sync Computing #dataengineering #databricks

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I heard confusion among the teams using snowflake in the last one week and thought of summarizing what’s the latest: New Snowflake tied attacks 1. Advance Auto Parts (new) 2. LendingTree Tree (new) 3. Ticketmaster ( last week) 4. Santander Bank ( last week) ------- So what’s going on? — attacker accessed a former Snowflake employee a demo account using stolen credentials — bypassed multi-factor authentication — claimed that they now have credentials of 100s of Snowflake customers — targeting a campaign directed at Snowflake users with single-factor authentication” more factual info: — attack includes 500 employee( snowflake customers) credentials with usernames and passwords — unique login page addresses for the above employees’ company’s Snowflake environments — these snowflake login pages are publicly accessible other possibly affected companies: — Allstate, Anheuser-Busch, Mitsubishi, Neiman Marcus, Progressive, Santander, State Farm ------- Snowflake’s take: — This is a targeted campaign directed at users with single-factor authentication and using credentials stolen from info-stealing malware or obtained from previous data breaches not related to Snowflake

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108 Recipies Snowflake Build: Just moments ago (Nov 12th, 12:30 PM CST), SF presented the Snowflake Dynamic Iceberg table using Polaris External CatalogSync." We have 20+ recipes in this e-book. Below is how to build summary tables as new data streaming into the base table. Snowflake

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Announcing Databricks AI/BI, a new type of BI product that deeply understands your data’s semantics and democratizes analytics. Built on a compound AI system that powers: - AI/BI Dashboards: AI-powered, low-code solution with all BI capabilities you'd expert out of the box, for answering a fixed set of business questions. - Genie: a conversational interface that can learn your underlying data continuously based on human feedback, and flexibly answer questions. Learn more about AI/BI's design and what this means for self-service BI.

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❄ Snowflake Vs Databricks 🏗 I've noticed a bit of a shift in the last 6 months or so. Databricks requirements seem to be (for us at least) more prominent than Snowflake. I had a really interesting chat with one of the founders of a leading Snowflake partner at Big Data LDN on where he thinks Snowflake is heading in comparison to Databricks. Is Databricks going to rule supreme? Will Snowflake be back? Is Fabric going to overtake them both? What are your thoughts? #snowflake #databricks #fabric

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