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Circle and Prime Days aren't just big for Target and Amazon — they move the entire retail sector. With Facteus data, you can see the impact in real time—before the market reacts. Don’t wait until it’s too late. Book your data test drive now. https://hubs.ly/Q03LWK7Q0 #marketintel #investortools #finance #fundamentals #quantfinance #financialplanning

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Donor expectations are changing — and so are the rules of fundraising. Join us on Tuesday, November 19 at 2:00 PM ET (11:00 AM PT / 1:00 PM CT) for a 30-minute webinar: From Campaigns to Fundraising — 6 Steps to Build Lasting Donor Loyalty. We’ll share a practical roadmap for moving beyond one-off campaigns to create truly supporter-first fundraising — with insights from real nonprofit success stories. 🎯 Understand your supporters more deeply 🔄 Build integrated journeys that strengthen loyalty 📈 Measure what really matters: lifetime value

Starbucks' $29.95 bear-shaped #Bearista cup sold out nationwide in hours – sparking overnight lines, customer brawls, and eBay listings of up to $50,000. But did the viral frenzy translate to real sales? 🐻 Our transaction data says…definitely. 📊 The Numbers: Launch day: +50.4% transactions YoY, $74.7M in spend Two weeks: $64M incremental revenue, 5M additional transactions YoY Sustained impact: Week 1 (+$53M), Week 2 (+$11M) So…why did it work? - Genuinely desirable (Instagram-worthy 'collectible') - Authentic scarcity (real demand, not manufactured) - Social currency (status symbol) - Perfect timing (holiday season) Now – two weeks later, Starbucks just launched a Target-exclusive Frozen Peppermint Hot Chocolate across 2,000 locations. The goal: drive Target foot traffic through beverage ‘exclusivity’. The problem? This collaboration lacks the Bearista's core success factors: - Commodity seasonal flavor (not distinctive) - False scarcity (widely available) - Distribution play vs. true demand creation Two struggling retailers partnering doesn't create new demand – especially if they share the same declining customer base. What’s the lesson? Exclusivity alone doesn't drive results. The Bearista created genuine desire through authentic scarcity and content virality. The Target drink redistributes existing demand – and likely creates minimal new. Real demand creation shows up in the data immediately: +50% transaction growth and $64M in incremental revenue . Distribution plays generate buzz but probably won't move the needle meaningfully – but time (and the data) will tell 👀 #Starbucks #Target #ExclusiveOffer #ConsumerData #ShoppingBehavior #Bearista #HolidaySpending Check out our full take on this in our newest blog 👇

🥉🥈🥇 Toward Real-Time Medallion Architecture One of the biggest advantages of open table formats like Delta Lake and Apache Iceberg is that you can query the same data with multiple engines. No vendor lock-in, just pick the best tool for each job. Case in point: We’re working with a customer who built their Medallion architecture using Apache Spark and Delta Lake. But as their business evolved, so did user expectations—especially around latency. Spark’s batch processing couldn’t keep up with the demand for fresh insights. The solution? We replaced periodic Spark jobs with the Feldera Incremental View Maintenance (IVM) engine. Feldera picks up changes to lower-tier tables and updates higher-tier tables in real-time, reducing the end-to-end latency of the pipeline from hours to minutes. Prior to open table formats, in the world of legacy databases, such a migration would require completely re-architecting the entire data stack in order to use a new query engine. But thanks to the use of Delta Lake, the transition was completely seamless to the rest of the company: ✅ The data hasn't moved and can be read using Spark, Trino, etc., just like before the migration. ✅ Same ingestion pipeline from Kafka to the Bronze layer. ✅ Existing BI dashboards reading the Gold layer stayed untouched—only now they show near-real-time data! Want to see how it works? Join Gerd Zellweger's webinar on replacing batch analytics with Feldera: https://lnkd.in/eFDqXb5Q

🔄 Incrementally Incremental Computation Incremental View Maintenance has always felt a bit sci-fi to me: a record changes in a SQL table, and the system instantly ripples that update through hundreds of SQL operators, refreshing every affected view. But what if not just the data could change incrementally… but the queries themselves? Traditionally, modifying queries meant throwing away all prior computation and starting from scratch. Not anymore. Feldera now supports incremental query modifications. This is an incredibly powerful capability. You can make arbitrary changes to your program — add, delete, or modify views — even if other views depend on them. Feldera will: 🔍 Identify all affected views ♻️ Discard any state invalidated by the modification, while preserving all other state 🧮 Use existing state in the system to compute new and modified views. The result is that many pipeline reconfigurations can be performed in a fraction of the time it would take to backfill the pipeline from scratch. One of our customers described this as "incrementally incremental computation." What a nice way to summarize a complex feature in three words! Learn more here: https://lnkd.in/gNSZY2j8

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Used by world-class product teams, from early-stage saas to global enterprises. Embeddable does the heavy lifting behind the scenes as your analytics grow. Share your project overview and start your poc today →

Whether you’re scaling your first saas product or operating at enterprise level, embeddable has you covered. We handle the heavy lifting so analytics never slow you down. Share your project overview and start your poc today →

Is your checkout process losing customers due to inaccurate or incomplete addresses? Egon Data Quality offers the ideal solution for your eCommerce checkout, with advanced address validation and geocoding. ✔ Reduce delivery errors ✔ Enhance customer satisfaction ✔ Boost conversion rates Integrate Egon and make every address count.

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Your AI future starts with your data foundation. To support AI, automation, and real-time insight, your platform needs a foundation built for data quality, governance, and scalability. Then, you’re ready to turn AI potential into business outcomes.

It’s time to rethink your data platform. The architecture you rely on today may not be ready for the demands of tomorrow. Modern demands require a unified foundation for cloud scalability, AI enablement, and resilient, governed data flows.

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How do you migrate to TcX without disrupting the business? At Realize LIVE Europe 2026, eQ Technologic’s on-demand session, presented by Gareth Webb, Vice President – EMEA Customer Engagements, showcases a proven approach to large-scale PLM migration and modernization. ✔ Legacy Teamcenter to TcX ✔ On-premise to cloud transformation ✔ Scalable architecture for high-volume metadata and files ✔ Enterprise-wide digital thread enablement And the proof? ⚡ Entire migration completed in 3 days using 500 parallel threads. If TcX, cloud transformation, or PLM modernization is on your roadmap, join us at Realize LIVE Europe 2026: 📅 June 29 – July 1, 2026 📍 RAI Amsterdam Convention Centre, Amsterdam, NL 📌 Booth #D24 Discover how eQube®-DaaS helps organizations transform with speed and confidence. https://hubs.la/Q04lQQLt0 #1eQ #eQTechnologic #eQubeDaaS #PLM #DigitalThread #CloudTransformation #RealizeLIVE

Amsterdam, we're coming. The eQ team is heading to Realize LIVE Europe 2026 and we're ready to talk PLM modernization, digital thread, Closed Loop Manufacturing, and Industrial AI. Meet our experts at Booth #D24: 👤 Gareth Webb – Vice President, EMEA Customer Engagements 👤 Alan Walker – Vice President, Strategic Engagements 👤 Martin Michel – Senior Manager, Business Development 📍 Booth #D24 📅 June 29 – July 1, 2026 📍 RAI Amsterdam Convention Centre, Amsterdam, NL Whether you're planning a TcX migration, connecting enterprise systems, or preparing for AI-driven transformation, we'd love to hear about your challenges and goals. Explore what eQ has in store at #RealizeLIVE Europe: https://hubs.la/Q04lyglm0 #1eQ #eQTechnologic #eQubeDaaS #DigitalTransformation #DigitalThread

I've been on a bit of a LinkedIn fast recently, but I've been brought back to tell you about our next #OTel panel where we discuss the status quo for projects that cater to end user client apps. Join Jared Freeze, Martin Kuba, Jason Plumb and me on July 22nd as we talk mobile, browser, sem conv, etc. Sign up here: https://shorturl.at/XDflC

Client-side OpenTelemetry finally caught its wave. 🏄 While nobody was looking, the OTel community poured serious investment into frontend: new semantic conventions for crashes, instrumentations built specifically for the browser runtime, and first-class support for Kotlin and Kotlin Multiplatform. Turns out mobile and web developers deserve telemetry too. Who knew. On July 22, I'm joining three people who actually build this stuff to get honest about where client-side OTel stands: - What's production-ready today (more than you might think) - What we're actively building right now - What's on the horizon Come for the roadmap, stay for four developers politely disagreeing about semantic conventions in real time. No wetsuit required. 📍 Register: https://shorturl.at/NAkDz 🗓️ Date: Wednesday, July 22 at at 10AM PT ⭐ Panelists: Hanson Ho: Android architect at Embrace, OTel Android approver, OTel Kotlin approver Martin Kuba: Staff software engineer at Grafana Labs, OTel JavaScript SDK approver and OTel Browser SDK maintainer Jason Plumb: Principal software engineer at Splunk, OTel Android maintainer, OTel Java maintainer, OTel Kotlin maintainer Jared Freeze: Software engineer at Embrace, OTel Browser SDK maintainer #OpenTelemetry #OTel #CNCF #Observability #MobileObservability #BrowserObservability

𝗘𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗲 𝘁𝗵𝗲 𝗵𝗶𝗴𝗵 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗵𝗶𝗻𝗱𝘀𝗶𝗴𝗵𝘁 Learn about the value of near real-time data processing in our free white paper. #Industryinsights

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A frontline manager and a CFO can ask your AI system the exact same question — and both need a different, correct answer. If your system can't tell the difference, that's not a model problem. It's a context problem. We built a free 20-question self-assessment for data and analytics leaders, covering: → Semantic foundations — does every core metric actually have one agreed definition? → Context ownership — who's accountable if an AI answer is wrong because the context behind it was? → Persona & role awareness — does the same question get the right answer for the right person? → Validation & governance — can you trace and defend how an AI-generated answer got made? Score yourself out of 20. Most organisations land somewhere in the middle — the gap is usually process, not technology. Download: The AI Context Readiness Checklist #emite #datacontext #aireadiness

Most AI pilots don’t struggle because the model isn’t capable. They struggle because the architecture underneath the model was never designed to scale. Connected data is not the same as understood data. Governed data is not the same as usable intelligence. And a dashboard is not the same as a decision. That’s why we built The Orchestration Architecture Playbook. It breaks orchestrated intelligence into seven practical layers: Experience Data → Enterprise Data → Integration & Processing → Semantic Context → Governance & Persona → Intelligence → Decision & Action And then turns that architecture into a five-phase roadmap organisations can actually build against. The goal isn’t to build everything at once. It’s to understand: • which layers your highest-value use case actually depends on • where data quality, correlation or governance will become a constraint • how to create a reusable integration backbone • where semantic context needs to sit • how persona and access controls become part of the intelligence itself • how to move from insight into a real operational action Because the value of orchestration doesn’t come from connecting more systems. It comes from connecting the right context to the moment a decision needs to be made. If you’re moving from AI experimentation toward production, this is the architecture conversation worth having. Download The Orchestration Architecture Playbook. #DataArchitecture #AIArchitecture #DataOrchestration #EnterpriseAI #DataEngineering #AIGovernance #Analytics #AskEmite #emite