Yordan Ivanov

Timescale Brings PostgreSQL into the GenAI Era with pgai Vectorizer https://lnkd.in/d6ztruWW #BigData #GenAI #pgaiVectorizer #PostgreSQL #Timescale

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💡 𝟑 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐚𝐛𝐨𝐮𝐭 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐃𝐚𝐭𝐚 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬 𝐲𝐨𝐮 𝐦𝐢𝐠𝐡𝐭 𝐧𝐨𝐭 𝐤𝐧𝐨𝐰💡 Join our CTO, Michał Misiewicz, as he explains three aspects of Customer Data Platforms in our latest video. Get a fresh perspective on how CDPs can enhance your data management strategies! 📈 📅 𝐌𝐚𝐫𝐤 𝐲𝐨𝐮𝐫 𝐜𝐚𝐥𝐞𝐧𝐝𝐚𝐫𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐥𝐨𝐮𝐝 𝐒𝐮𝐦𝐦𝐢𝐭 at PGE Narodowy on November 6th. Michał will be presenting at 3:00 PM, where he'll share our journey in building one of the largest CDPs in Poland. See you there! 👋 #CustomerDataPlatform #GoogleCloudSummit #DataManagement #TechInnovation #CDP #Datumo #BigData #Cloud

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Last week we shared our BRAND NEW whitepaper: Managing Madtech Data Complexity: A Data Infrastructure Buyer's Guide. If you haven't had a chance to dive in yet, now's the perfect time to get ahead of the curve in navigating the evolving data landscape of our industry. Here's what you can expect to uncover within the pages of this insightful whitepaper: 🔍 Comprehensive Overview: Gain a deep understanding of the data infrastructure landscape, empowering you to make informed decisions. 🚀 Emerging Trends: Stay ahead of the curve with insights into the latest trends shaping the future of data infrastructure. 🔒 Ensuring Scalability, Performance, and Compliance: Learn top considerations to ensure your data infrastructure is equipped for scalability, high performance, and regulatory compliance. 🔎 Vendor Overview and Selection Criteria: Navigate the maze of vendors with confidence, armed with key selection criteria to identify the perfect fit for your needs. Be among the pioneers in your industry to leverage this invaluable resource as we collectively navigate the data changes ahead. Don't miss out – grab your copy today and stay ahead of the curve! https://lnkd.in/e7e95iis

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Want to know more about Matillion <> dbt? In this three-part series we explore an intro to Matillion and dbt, testing data quality, and getting into AI. Take a look: Pt 1 https://bit.ly/4dQ2D11 Pt 2 https://bit.ly/4cTW9x2 Pt 3 https://bit.ly/4dykubO #dbt #elt #DataEngineering

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Challenged by RAG systems that sometimes miss the mark? ✖ The culprit is often the unstructured data you feed to your system. We’ve unpacked strategies to use data enrichment to hypercharge your RAG's precision. 🚀 Here's how you can transform confusion into clarity: 🎯 Named Entity Recognition for pinpoint accuracy  🔑 Keyword generation to give structure to unstructured data  🤔 Topic modeling for thematic insight  🔗 Link contextualization for richer connections Data isn't just fuel—it's the GPS for your AI's journey. Refine it, and watch your RAG system improve accuracy and precision. Your AI isn't lost—it just needs better directions. 🧭 https://lnkd.in/eu8fMPhh #AI #DataEnrichment #RAG #MachineLearning #DataScience #ArtificialIntelligence

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You can now run an entire data stack in Snowflake. A few weeks back we discussed how this works and how you could run a dbt project in an ad-hoc way. Let's now make sure this is scalable and rigorously implemented. That means we need to think about CI/CD, error notification, run monitoring, and container tracking. These are all game changers that we are super stoked about. Marcello Victorino is walking us through the setup next week, but below you can already see a small preview. See the whole post here: https://lnkd.in/gpwZ-Cws

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What if your supergraph could understand natural language? In this new talk on building an AI-enabled supergraph, explore how: 💡 Generative AI enables text-to-query and text-to-schema capabilities 💡 GraphQL APIs empower large language models with real-time context and actions 💡 Text-to-action workflows can automate and streamline your processes 🔗 Watch now: https://bit.ly/4hXm6PM

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Maintain accuracy in production with ML models that always learn from the newest data in the data warehouse. #whykumo Request a demo and query the future: https://kumo.ai/apply #ProductionAccuracy #DataLearning

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What is a principal component analysis (PCA)? Good question. A PCA is a form of data engineering that we use here at Postie to condense large data sets to make them easily mappable when building advanced audience clusters for DM campaigns. Whoa, that was a word jumble if we've ever read one. Let's slow it down for a sec. 😮‍💨 Imagine importing a large data set with thousands of data variables. Then, cross-reference those variables with all the contacts in your data set and try to find actionable patterns to put your contacts in and run marketing campaigns off of. Houston, we have a problem. But the problem wouldn't be struggling to uncover patterns. 🚫 On the contrary! The problem would be having so many patterns (think MILLIONS) that you would be drowning in data and have no clue where to start. Talk about decision paralysis! We're merely humans, after all. 👩🏼 This is where a PCA comes into play. Instead of considering every variable in your data set, a PCA isolates the variables that indicate the greatest variance between your contacts. These are called principal components. 💡  Then, it uses those principal components to cross-reference all the contacts in your data set and plot them on a nice, neat little graph where you can build your clusters to send to. Now, this is a bit of a simplification, but it paints an appropriate picture of why this tactic is so critical when performing advanced clustering. Without it, there's NO WAY you could successfully analyze all the data and have an actionable output that would deliver on performance like only direct mail can. #datascience #postie #clustering #modeling #targeting #digitalmarketing

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Master the magic of enhancing your data pipeline's using backfilling. 🧙‍♂️ ✨ Checkout Cole Freeman's blog to learn how to backfill your data pipelines using Mage.

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Looking to set up a production-ready pipeline from scratch using #Kafka and #Snowflake, the essentials of modern #data pipelines? Read about our best practices in the #Atlantbh blog by Darko Kojović 🔗 https://lnkd.in/dH6MVY-u

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New Feature Spotlight: Column Lineage Search 🔍 It's now even easier to follow your data's journey with OpenMetadata 1.4. Search lineage by column names, and trace the upstream ⬆️ and downstream ⬇️ nodes with column level granularity. Demo Video: https://buff.ly/3wX42CS

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Documentation is a great starting point to begin your journey with ExyData. You can use the #ExyData platform in different ways: - ExyData Web Console: Deploy your infrastructure in minutes using our intuitive Web Console. If you prefer a graphical interface, this is the best way to get started with ExyData. - CLI: Deploy your infrastructure swiftly using our CLI. It's perfect for local machines or integrating into your CI/CD pipeline. - API: Utilize our API to deploy your stack in minutes. Integrate it with your workflows and other tools to automate deployments effortlessly. Which method do you prefer? Remember, you can access a $250 credit to try the platform. Get started today or reach out to Rudy Argüello or Ignacio Van Droogenbroeck to discuss your use case. #DataAnalytics #Analytics #DataPlatform #Hosted #Managed

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New large language models (LLMs) are going live frequently, and many have accomplished explosive growth💥, but this has left companies with uncertainty around what costs they will incur to bring their AI dreams to market 🤔 Enter BeFOri, the benchmarking framework from Ori that helps you answer your GPU budgeting questions ✅ Learn how to use BeFOri to project #LLM inference costs for self-hosted models and calculate a cost-per-token to compare with third-party API providers ⚖💰 Check out the blog post: https://lnkd.in/du5rGmJU

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Schema migrations are often complex and risky, but they don’t have to be. At PGConf EU, Xata Staff Software Engineer, Andrew Farries shared how the Expand-Contract Pattern enables zero-downtime PostgreSQL schema changes. He also introduced pgroll, an open-source tool that automates this process, making even the most challenging schema migrations seamless. Our latest blog recaps Andrew’s talk and includes a video demo showcasing pgroll in action. Learn how to simplify schema changes, avoid downtime, and handle large datasets without locking your tables. Check it out here: https://lnkd.in/e8vbpQTS

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Sara Gawlinski here, taking over the dbt Labs LinkedIn with some exciting news: unit tests are finally LIVE in dbt 🎉 You've always had tests to catch whether or not a model will run in production, but now with unit tests, you can validate your model logic before it even hits your data platform. That means you’re not just asking “Will it build?” but can include the nuance of “Will it do what it’s supposed to?” 🧠 If a unit test fails, the model won’t build—saving you from unreliable data products and unnecessary platform costs. Check out this quick video showcasing this game-changing feature, and start building performant, cost-effective, and reliable models today 🥇

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