Jordan Meyer

Our Lightning Talk at Databricks #DataAISummit is sold out! 😊😊 Let us know if you'd like to schedule time with our team while we're in #SF. ⌚ #GenAI #Data #AI

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Cool article from amazing engineers Rithvik Panchapakesan and Danmei Xu about search, embeddings and quantization! These are the folks powering Snowflake's semantic search! #snowflake #vectorsearch #ai

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You know a Semantic Layer would be hugely valuable, but how do you actually build such a thing? Pro tip: Crafting a Semantic Layer is about building iterative velocity alongside accuracy, so that when your stakeholders ask about Revenue MoM grouped by Attribution Channel, you can answer instead of adding a ticket to the backlog. Start with these four steps: 1. Identify a Data Product that is impactful: Find something that is in heavy use and high value, but fairly narrow scope. Don’t start with a broad executive dashboard that shows metrics from across the company because you’re looking to optimize for migrating the smallest amount of modeling for the highest amount of impact that you can. For example, a good starting place would be a dashboard focused on Customer Acquisition Cost (CAC) that relies on a narrow set of metrics and underlying tables that are nonetheless critical for your company. 2. Catalog the models and their columns that service the Data Product, both in dbt and the BI tool, including rollups, metrics tables, and marts that support those. Pay special attention to aggregations as these will constitute metrics. 3. Melt the frozen rollups in your dbt project, as well as variations modeled in your BI tool, into Semantic Layer code. 4. Create a parallel version of your data product that points to Semantic Layer artifacts, audit, and then publish. Creating in parallel takes the pressure off, allowing you to fix any issues and publish gracefully. You’ll keep the existing Data Product as-is while swapping the clone to be supplied with data from the Semantic Layer. Dig deeper into the step-by-step process of how to ship a Semantic Layer in pieces at our link in the comments.

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Want to learn more about how to get started using Snowflake Cortex LLM Functions? Check out this post from Jon Coulter detailing how to use these tools to quickly classify data, empowering Data Engineers with Data Science tools! https://lnkd.in/gMTFT5xQ

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Data scientists: Are you spending too much time wrangling data instead of driving insights? Our white paper explores how combining Snowplow and dbt can revolutionize your data modeling workflow. Learn how to: 🧩 Design flexible data models that evolve with your business 🚀 Build, test, and deploy models efficiently 🔍 Unlock deeper behavioral insights from your data Learn how to take your data strategy to the next level by downloading the white paper now: https://lnkd.in/euqMeYsR #DataScience #Analytics #DataModeling #ModernDataStack"

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Is the age of #PowerPoint ending? For your next presentation, consider using #Quarto. See below for some tips from Sophie Lee, PhD, FHEA 👇👇👇

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How can Tempered AI help you with adopting Databricks? 🤔 We have certified consultants that can deliver or speed up any project: ➡ Migrating to data and pipelines 🔄 Set up CI/CD best practices 🍱 MLOps (Yes, it's a bento box representing the varied components) 🤖 Create a RAG based chatbot? We are a small, newly-established and boutique firm of enthusiastic consultants focused on Databricks. We don't bloat costs as we are light on management and heavy on technical ability. We solve problems because we enjoy solving them. Got burnt working with a consulting firm that was all fluff? Experience what's it's like working with a firm whose vision is #TechFirst! The #Databricks professional services team trusts us to deliver projects, and so can you!

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✨ Master AI in Snowflake: Enhance Your DBT Workflow ✨ Our Senior Data Engineer, Jaime Andres Salas, guides you in integrating LLMs with Snowflake Cortex into your DBT transformations. Key points: 🔹 Sentiment analysis with AI 🔹 Join sentiment data with reviews 🔹 Create recommendations for feedback https://hubs.ly/Q02BX1sW0 👉 Read the full article #AI #Snowflake #DBT #DataScience #LLM

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I really enjoyed my conversation with Bryan Wood and Jeremy Kuhnash from RedPanda at the Databricks conference! I remember bumping into Redpanda when I was researching and writing my ebook for Redis (Redis Streams vs. Kafka - a visual guide) nearly 2 years ago! https://lnkd.in/ga4vbvaN For those who don't know, RedPanda is a faster, a more cost-effective, and a drop-in replacement to Kafka. For example: say for some workloads, you may need 10 Kafka brokers, with RedPanda, you'd need just 3 - that's a huge savings! So if you are thinking of using Kafka or Confluent, you should definitely check them out! https://redpanda.com #kafka #confluent #streaming Databricks

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"The Data Team Playbook: 50 Resources" by Seattle Data Guy offers a comprehensive guide filled with tools and best practices for data professionals. A good resource to explore this Friday and to wrap up your week. Worth checking out for anyone involved in data team operations!

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❄️🔥 Snowflake is, ironically, a hot topic. (Also a cool one.) 🏎️ Onehouse makes it way faster and much less expensive to use Snowflake. 🌃 And Onehouse, with Apache XTable (Incubating), opens up access to ALL data lakehouse formats: Apache Hudi, Apache Iceberg, and Delta Lake. ⏱️ Today - ahora mismo, as they say in Spanish - not off in the future. ✍️ To find out how, sign up for our webinar this Thursday. See you there! https://lnkd.in/gJEExsyJ #onehouse #dataengineering #nolockin #universaldatalakehouse #apachextable #opensource 

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We're thrilled to announce our latest YouTube tutorial on How to Create a Free Account in Databricks Community Edition! If you're looking to get started with Databricks, this video is the perfect guide for you. Whether you're new to Databricks or looking to explore its capabilities without any cost, this video will help you get up and running quickly and efficiently. Don't forget to like, comment, and subscribe for more tutorials! 📺 Watch the full video here: https://lnkd.in/gk_RtAFp #Databricks #DataScience #BigData #MachineLearning #TechTutorial #CommunityEdition #DataAnalytics #FreeResources

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Databricks' new Tabular Play has put Snowflake on the defensive, sparking fresh competition in the cloud data landscape. Curious about what this means for the future of cloud computing and data analytics? Dive into the full story to get all the insights! https://lnkd.in/gwdGJycD #CloudComputing #DataAnalytics #Databricks #Snowflake #TechNews #BigData #DataScience #MachineLearning #AI #DataWarehouse #DataLake #CloudStorage #Innovation #TechTrends #BusinessIntelligence #DataEngineering #CloudSolutions #ITInfrastructure #DigitalTransformation #TechIndustry #DataManagement #DataStrategy #FutureTech #TechUpdates #EnterpriseTech

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Ready to bring consistency to your metrics with the dbt Semantic Layer? Join one of our live, 2-hour interactive courses on Tuesday, April 30th at 12pm ET or Wednesday, May 1st at 1:30pm AEDT! Designed to improve your data project management, you'll gain a clear understanding of what the dbt Semantic Layer is, its purpose, and when to use it in your projects. Here's what you'll get by attending live:  ✅ Discover how to query the dbt Semantic Layer from Google Sheets and other BI tools, enhancing your analysis and data-sharing capabilities. 📈 Learn how to set up entities, dimensions, and measures in your semantic models and how to effectively configure and use metrics. 🤝 Get live support (because we've got your back) —ask questions, join discussions, and code along with us. Speakers include our resident dbt experts Faith Lierheimer, Alex Dinsmoor, Michelle Baird, and Joel Labes. Can't make it to the live event? No worries! Register anyway, and we'll send you the recordings and recap content so you can experience all the action on-demand ➡️ https://bit.ly/4aYKVqy

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Many-time bestselling author and prolific open-source R developer Hadley Wickham is our guest today. In it, we discuss Posit's rebrand and why the Tidyverse needs to be in every data scientist's toolkit. More on Hadley: • Chief Scientist at Posit PBC • Adjunct Professor of Statistics at Stanford University, Rice University and The University of Auckland. • Is best-known as the creator of the Tidyverse suite of open-source R libraries for data science, including the essential libraries dplyr and ggplot2.  • Has written seminal books on R programming for O'Reilly, Springer and CRC Press, including the mega-bestselling "R for Data Science". 📚 BOOK GIVEAWAY I will personally ship 10 physical copies of Hadley's books to people who comment or reshare this LinkedIn post. Simply mention in your comment or reshare which of these books you’d like: • “R for Data Science” • “Mastering Shiny” • “Advanced R” • “ggplot2” • “R Packages” I’ll hold a draw to select the 10 book winners next week so you have until Sunday, May 5th to get involved in this book contest. (For this to be feasible, the book must be readily available for online sale in your country; I won't, for example, try to ship the book over a border because this leads to customs headaches. If I can’t get a physical book to you easily, I'll try to buy you a digital version instead.) Today’s episode will primarily be of interest to hands-on practitioners like data scientists and machine learning engineers. In it, Hadley details:  • Why the iconic open-source company RStudio rebranded to Posit. • The philosophy of the tidyverse, amusing backstories on its most iconic packages and why the tidyverse is invaluable for all data scientists to be familiar with. • The open-source projects he’s most excited about today. • How you can easily get involved with career-bolstering open-source projects yourself. Many thanks to Intel Corporation and Hewlett Packard Enterprise for supporting this episode of Super Data Science, enabling the show to be freely available on all major podcasting platforms as well as the video version we publish on YouTube. This is Episode #779! #superdatascience #datascience #machinelearning #statistics #rlanguage

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Common Agentic workflow patterns #llm #llms #agenticworkflow

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In Episode 14 of "Friday Fun", we discuss a use case of time series forecasting for our hypothetical client, "Lessons in life Inc.". You can watch the episode here: https://lnkd.in/gdNuDZMB Previous episodes can be watched here: https://lnkd.in/gYpX5UgW #data #analytics #ai #ml #datascience #designedanalytics #forecasting

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What makes our Artificial Intelligence forecasting different than the rest? We can explain it. Learn more from Dr. Ben Toms about our powerful forecasting method:

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Modern GTM Data should look like modern data for LLMs Harry Stebbings episode with Alexandr Wang a few weeks ago smacked us in the face with this. Alex is the leader in the #AI data space. He said frontier data is what is going to power these models going forward. New data, complex data and data that is created by leveraging our intellectual capabilities in reverse engineering how we as humans would interact with models.  Chain-of-thought prompting is a prompt engineering technique that aims to improve language models' performance on tasks requiring logic, calculation and decision-making by structuring the input prompt in a way that mimics human reasoning. This is required after you apply the reasoning that enables AI models to break down complicated problems into easy-to-handle intermediate steps, leading them to solve complex multi-step reasoning problems. Wait? Didn't I just do this in Clay in a demo two weeks ago? Isn't this all the rage in GTM data? YES - it is. GTM Architecture isn't changing - it has changed. The architect is the most important player right now. We need to reverse engineer how to be successful with GTM data which means we get meetings, we provide value and we get a mutual exchange with our target audience in a consistent and repeatable manner.  You need the basic building blocks to chain these thoughts together: 𝐃𝐚𝐭𝐚: Can't just use the same old data providers and win. Dozens of data sources need to be combined, enriched, verified and 𝐚𝐧𝐚𝐥𝐲𝐳𝐞𝐝 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫. 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧: This can all be automated. No need to pay someone for outcomes you expect from them when they have to design this manually and at best will take them 40% of their time but they have no experience in this. It isn't their fault! 𝐈𝐧𝐩𝐮𝐭: What do you do to the data that makes it more meaningful? Hiring, spending, technology, patterns, competitive information.  So much more. 𝐀𝐈: Yes. We need #AI and it should be used. However, prompting is a skill and needs to be done right and yes - humans need to be in the loop 𝐄𝐦𝐚𝐢𝐥 𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞: You can't rely on the old way of delivering. Need multiple domains, need them rotating, need verified emails and you need a system that runs lights out that you don't support 𝐓𝐫𝐢𝐠𝐠𝐞𝐫: When is the best time to fire my cannon? 𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐜𝐲: Why does this even matter, at what point, what is the value? 𝐕𝐨𝐥𝐮𝐦𝐞: Less will be more. Shotgun approach and spray and pray looking at open rates and applying the wrong math won't work. A lot has changed. The challenge is fun. Data used in AI systems can teach us GTM folks a lot. When patterns tell you what is going to happen - listen to them.

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