LinkedIn video ad examples

I've been wanting to improve how the sea looks on our base map styles for a long time - especially for our a maritime customers. After a series of rewrites I think I've finally found an approach I'm happy with. It works with all our map styles, and can inherit the colours of the existing map. Keep up to date with the regular updates: https://lnkd.in/ecpnG2nU #IconMapForFabric #MapsInFabric #MicrosoftFabric #IconMap

What will your construction project look like from the surrounding community? Before a wind turbine, power line, road, or new building is constructed, understanding its visual impact can be critical. The viewshed tools in #IconMapForFabric make this analysis interactive. Choose a location and height, then instantly see where the object would be visible from. Highlight affected buildings and areas, or select impacted features directly from a map layer. You can also analyze multiple objects together, creating a combined viewshed for an entire development rather than assessing each asset individually. It turns a complex spatial question - “Who will be able to see this?” - into an interactive visual analysis. Icon Map for Fabric launches soon - sign up to keep up to date: https://lnkd.in/ecpnG2nU #MicrosoftFabric #Geospatial #Construction #GIS #LocationIntelligence #SpatialAnalytics

Everyone's prompting. Few teams have AI workflows that produce the same answer twice. In this free three-part Demo Dive Series, @Mikkel Dengsøe shows how data teams go from ad hoc prompts to reusable skills to production playbooks with the Coalesce MCPs. The answer isn't less AI. It's structure. Live Oct. 20, Oct. 29, and Nov. 10 at 10 a.m. CET. One registration covers all three sessions, plus the recordings.

Learn how to use agents and MCPs to automate daily data engineering workflows. Trace a broken table back to the commit that caused it. Turn a blank SKILL.md into a weekly data quality report in Slack. Then put it on a schedule. That's the Agentic Data Engineering Demo Dive Series: three live sessions of real demos with the Coalesce MCPs, run in an MCP client like Claude Code. Register today to attend the whole series, plus get access to the recordings and practitioner's guide.

Are your AI agents ready for financial crime investigations? Ex-HSBC leader Michael Shearer weighs in. Read his paper on the pitfalls.

Your client asks: "Are you sure it's backed up?" You shouldn't have to hope. Exaba delivers immutable, ransomware-resilient backup storage built for MSPs. So the answer is always yes.

The next evolution of cloud isn't hyperscaler. It's LocalScaler. Exaba enables MSPs to build high-performance local storage clouds with greater control, predictable economics, and effortless operations.

A short tech/business overview of what you can do with Inphinity Planning in Qlik (both, On premise and Cloud). Created to solve real planning challenges, it uses Qlik dimensions and measures respecting section access and other #Qlik rules. And there is much more.. Got a use case there? #analytics Inphinity ∞

Bir siber saldırının ortalama maliyeti 200 milyon dolar, gelirin yüzde 9'una kadar. Hazırlanmak için videonun tamamını izleyin.

Your AI sees your code, not what actually ran. DataFlint Spark Copilot reads your real Spark runs. Setup is one URL. 👉 Try it yourself: https://www.dataflint.io/ #ApacheSpark #DataEngineering #AI

How do you decide between synthetic data and masked production data? During the Q&A of our DATPROF Customer Webinar yesterday, I discussed this question with Bert Nienhuis. It’s one of the most persistent debates in Test Data Management (TDM), but in reality, you don't have to choose. Use synthetic data early in your SDLC for fast unit testing and new features where production data doesn't exist yet. Switch to masked, subsetted production data further down the pipeline for integration and regression testing, where real-world business logic and historical edge cases are essential to catch critical bugs. Stop looking for a single magic solution. True TDM maturity is about placing the right tool at the right stage of your delivery pipeline. 👇 Want to watch the full Customer Webinar? Link in comments.

While Agentic AI can reason, it still needs an external compute resource to run its logic. Such logic may include geospatial computations. 🧠 The future of spatial AI depends on connecting the intelligent reasoning from LLMs with a solution that can run any geospatial logic against any geospatial data in a scalable way. #AgenticAI #GeospatialData #SpatialComputing

Putting everything into one system sounds simpler. But does every type of data actually benefit from being managed in the same way? 🤔 Satellite imagery, sensor streams, and analytical datasets all come with different access patterns and performance requirements. Trying to optimize one infrastructure for every workload can quickly create trade-offs. The more useful question may be how to make different types of data work effectively together across the organization. 🌍 #Geospatial #DataInfrastructure #SpatialData

Quelles sont les plus grandes news de l’année chez ClickHouse? On a tout dit sur DataGen au micro de Robin Conquet! Au menu : 🤖 Comment l'arrivée des agents IA impacte ClickHouse, la base de données et l’observabilité 🚀 La création d’une Agentic Data Stack avec le rachat de Langfuse, leader dans l’observabilité des agents IA 🧑💻 L'extension vers une plateforme data & IA complète (Iceberg support, CDC, streaming queries) L'entretien en intégral: youtu.be/EZ-dEA-mDGs

Your competitor is probably using the same AI as you. 🤖 The advantage won't come from the model - it comes from the data behind it. Our free 5-part webinar series shows why trusted data is the real competitive edge in the AI era, with Tobias Pentek, AI expert at CDQ. 📅 Starts October 8, 2026, live Thursdays, 3:00 PM CEST ✅ Free · 5 episodes 🎥 Recordings emailed automatically 🎁 Bonus: live CDQ Genius demo in episode 5 Register once, get access to all 5 episodes.

KI-Modelle sind keine autoritative Quelle - sie sind nur so gut wie die Daten und Regeln darunter. ⚠️ Kai Hüner, CTO von CDQ, erklärt in unserer kostenlosen Webinar-Reihe, warum KI ohne verlässliche Daten, Fehler nur schneller und teurer macht und wie Sie das vermeiden. 📅 Start: 6. Oktober 2026, live dienstags, 15:00 Uhr ✅ Kostenlos · 5 Folgen 🔍 Praxisbeispiele aus echten Projekten 🎁 Bonus: CDQ Genius live in Folge 5 Einmal anmelden, Zugang zu allen 5 Folgen.

Det er skræmmende, hvor meget man kan betale for data 😨 og hvor lidt man i realiteten kan ende med at få ud af det! Netop derfor tilbyder vi ubegrænsede leadlister - til en fast lav pris. Data skal betale sig selv tilbage i øget salg, sparet tid og bedre indsigter. Og så skal det være opdateret... Ellers giver det ingen mening at bruge penge på! Læs mere her: https://lnkd.in/ePYzzema

Vil du gerne springe over, hvor gærdet er lavest? 🫏 Jeg har i alt for mange år manuelt håndteret salgsemner til outbound og marketing. Du ved, de der dage, hvor du sidder og VLOOKUP'er, CSV-ificerer, fjerner dubletter og krydser fingre for, at der ikke er fejl i dine efterhånden uendelige Excel-formler 😴💤 Og i den anden ende af kontoret, sidder der en utålmodig mødebooker eller salgschef og venter på deres opdaterede filer. Det løber mig koldt ned ad ryggen, når jeg tænker på alle de timer, jeg har brugt på det. En af vores kunder sparer 30 timer om måneden på datahåndtering. Det er næsten en hel arbejdsuge - hver måned! Derfor startede vi DataPeeps. Så du bruger din tid på salg – ikke på at gøre data klar til salg. DataPeeps.dk

Crescer sem qualificar dados não transforma volume em escala. Transforma volume em risco. Para instituições de pagamento, gateways e subadquirentes, cada novo cadastro, transação e relação comercial exige informações consistentes e confiáveis. Sem uma validação adequada, o crescimento da operação também pode aumentar: • Tentativas de fraude • Decisões baseadas em dados incompletos • Retrabalho operacional • Exposição regulatória A DataHub apoia o ecossistema de pagamentos com soluções de validação cadastral, análise de risco e enriquecimento de dados. Conheça melhor quem entra na sua base e aumente a segurança das suas operações.

Sua operação de pagamentos está crescendo. A validação dos seus clientes acompanha? Cadastros sem validação e dados inconsistentes abrem espaço para fraudes, retrabalho e decisões que colocam o negócio em risco. A Datahub reúne validação cadastral, análise de risco, KYC e enriquecimento de dados para apoiar uma operação mais segura, em escala. Clique em Saiba mais e conheça as soluções da Datahub para sua empresa.

Today we're introducing Cockroach Continuum, built for an era when agentic applications are growing database estates faster than any team can manage by hand. Bring a workload, and Cockroach Continuum folds it into the shared estate — pooling capacity across thousands of isolated databases instead of provisioning each one for its own peak. Cockroach Plenum, our usage-based storage layer, means you pay for the storage workloads actually use rather than what gets provisioned. Cockroach Aegis, a system of agents, watches the estate continuously, observing, recommending, and, under your governance, acting with growing autonomy over time. The foundation is CockroachDB, the same engine trusted in production for a decade. Cockroach Continuum is what it evolves into next. 🪳 Alongside Cockroach Continuum, we're unveiling a new look. A visual identity built on the same principles as the product: resilience, adaptability, and technology designed to stay up for anything. https://lnkd.in/eAWijr2q

Dashboards are unfortunate creatures. Nobody wants them to be what they are. Executives wish they were self-updating slide decks. Instead of charts in a boring grid, they wish for dazzling visuals, a big header, one metric per tab, while still seeing the applied filters and changing them on the fly. Marketing wishes it were a story. Instead of rows of numbers, they wish for a narrative so convincing that everyone nods along to whatever wild idea comes next, and agrees that the metrics colored in red are probably the market's fault. Revenue wishes it were a crystal ball, handing out predictions and projections built on historical CPL, CAC, and whatever else it can get its hands on. They all wish the dashboard could be more. But most of the time a dashboard is the place to look at, not the place to act on. Now it can be more. Our new HTML Layout frees you from the fixed canvas. You can just use HTML and CSS to build highly tailored data experiences - storytelling slides, print-perfect reports, or even data game. This update also makes Holistics much more vibe-coding-friendly: the output is now plain HTML, a language AI already writes fluently, rather than a proprietary layout format. Soon you'll be able to just describe the data experience you want and Holistics AI will build it for you.

"I find your AI to be surprisingly accurate". A customer told us this a while back, and it's a compliment we've had mixed feelings about ever since. The "accurate" part we're glad to hear. An AI chatbot that isn't accurate doesn't have much reason to exist. That’s its entire job. But why the surprise? Is it because the gap between what data vendors promise and what they actually deliver has grown to the size of a sinkhole - big enough that an AI simply not making things up now counts as a pleasant surprise rather than the baseline? Or is it that we haven't done a good enough job marketing our AI? The former is collectively an industry problem, and not much we can do about it on our own. The latter, though, we can control. So here's something you might not know our AI can do: EVERYTHING, actually. In this 3-minute video, our product manager shows how he used our latest feature, Local Agentic Development, which lets you bring the coding agents you already know - in this case, Claude Code - and pair them with Holistics through MCP to go from raw schema to a fully functional, well-governed dashboard in just a few minutes. Have a look. And if you already use Holistics, give it a try.

Trace #sampling allows you to harness the power of distributed tracing without breaking the bank, but also adds inaccuracies; let's talk about how you fix that. There are two commonplace ways of doing sampling: probabilistic #HeadSampling and #TailSampling. With probabilistic head sampling, it is like you throw a die for every trace, and keep the trace only if the die's face is low enough. With tail sampling, you collect the traces and keep those that are *interesting*, typically slow ones and erroneous ones. Head and tail sampling reduce the accuracy of your data, over or underestimating error rates and latencies of your services. (Btw, creating animations with LLMs is pretty addictive.) What can you do to reduce inaccuracies? You additionally record rate, errors and durations (RED) metrics for your services on the full data before sampling. With #OpenTelemetry, you can do trace sampling and #RED metrics collection with the same pipeline setup. Sometimes you also combine the two methods: tail sampling for interesting traces and probabilistic head sampling for baselines. However, processing the resulting data in alerting and dashboards is usually a challenge, while RED metrics make it easy. I wrote an article about this a little while ago that is still pretty relevant, and there is also an accompanying talk, which I fearlessly link in the main post: * https://lnkd.in/eKJCyuXy * https://lnkd.in/eRaczB4N What else do you do to improve the accuracy of trace sampling?