B2B ad examples

An unsere Sportwagenfreunde, wäre sicherlich ein tolles Erlebnis, aber mit 3 Kids nicht meine Klasse. Dann nehme ich lieber das zweite, sollten wir zumindest reinpassen und unser Ziel erreichen. Psst 🫢 Aber daraus gibt es ja immer noch das Gerücht das eine zentrale und konsolidierte Datenschicht nicht benötigt wird. Kann gar nicht verstehen warum?

An honest question: how many AI strategy decks have you sat through in the past six months? Now consider two hours where someone shows you the actual thing running, on real reinsurance data, with the human in control at every step. That is 17 June, in Hamilton. Link in the comments.

A question for the CFOs and CIOs in the reinsurance market: if the BMA or CIMA asked tomorrow to walk you through the data, lineage, and approvals behind any AI in production, how much of it could you actually show them? We are running a live seminar on 17 June for the people whose job it is to have an answer.

Que tal potencializar ainda mais a governança, a segurança e a eficiência do seu processo? Com recursos como integração flexível de dados, conectividade universal e suporte para GenAI, a plataforma oferece uma base sólida para decisões assertivas e análises mais rápidas. E aqui na Kriptos, estamos prontos para ajudar na sua jornada! Nossa expertise em soluções de dados e governança pode acelerar a transformação digital da sua organização. Vamos conversar sobre como podemos colaborar! https://lnkd.in/dXG5SwDs #GovernançaDeDados #Denodo #DataVirtualization #TransformaçãoDigital #IA

Na gestão de informações, eficiência e precisão são fundamentais. Muitas empresas ainda dependem de planilhas no Excel para gerenciar dados importantes, mas será que essa é a melhor solução? Na Kriptos, ajudamos sua empresa a ir além das limitações das planilhas com soluções avançadas e dashboards interativos que facilitam a visualização dos dados oferecem maior precisão e eficiência operacional. Está na hora de abandonar as planilhas e evoluir para uma gestão mais inteligente! Vamos conversar? 💬 https://lnkd.in/gSEnTUqJ #Inovação #Eficiência #Automação #Dashboards #GestãoEmpresarial #Kriptos #Tecnologia

Os melhores dados do mercado estão aqui. Fale com o nosso time e conheça a BigDataCorp!

Como empresa parceira BigDataCorp, você amplia seu portfólio com soluções inovadoras, gera novas oportunidades de receita e entrega ainda mais valor para seus clientes.

Stop fighting broken pipelines. BettrData automates ingestion, delivery, and compliance so your team can save 30–40% of their time.

Your team’s best work isn’t happening in spreadsheets. Automate 70% of repetitive data tasks and give your people time to innovate where it matters most.

Echte resultaten komen voort uit expertise in data-ecosystemen. Dat vraagt meer dan tools. Het vraagt vakmanschap in AI-oplossingen. 💎 Bij BigData Republic doe je dat samen met gelijkgestemden: Data Visionaries die verder kijken dan de gebaande paden. Je brengt orde, bouwt oplossingen die echt gebruikt worden en zorgt dat ze goed blijven draaien in het dagelijks proces. We werken in kleine teams met korte lijnen. Je hebt invloed op de aanpak, leert van collega’s en groeit elke stap mee. Nieuwsgierig? Ontdek werken bij BigData Republic. 🚀 #DataVisionaries #DataEngineering #MachineLearning

Waardevolle data-oplossingen ontstaan niet vanzelf. Die bouw je met kennis, precisie en doorzettingsvermogen. Bij BigData Republic ga je verder dan ‘even iets neerzetten’. Voor topmerken leg je de basis, bouw je de oplossing en zet je ‘m in productie – met zicht op prestaties, feedback uit de praktijk en continue verbetering. Kleine teams, korte lijnen, invloed op technische keuzes en aanpak. We leren, experimenteren en groeien samen. Klinkt als jouw speelveld? Maak kennis met werken bij BigData Republic. 🚀 #DataVisionaries #DataEngineering #MachineLearning

Unstructured data -> product via AI primitives. We manage the latency, compute, monitoring, and fine-tuning so you can just deploy workflows and enjoy the 100% accuracy.

Turn unstructured data into product with your new semantic infrastructure layer. Used by startups to Fortune 100 orgs.

Most BI tools were built for analysts. Milo was built for everyone else.

If your team spends more time building dashboards than making decisions, it’s time for something different.

Build it? Scraping, parsing, NLP, vector storage, monitoring, maintenance. Or license it - one API call, 25+ signal types, production-ready, always updated.

50+ data teams already license our signal data. If you're building a sales or data product, we have the signals your customers need - delivered via API or flat file.

The inputs plan is due. Your confidence with the sub-field variation to build the prescription is low. You use the last date you had a clear image and hope growth pattern is the same. The farmer gets a plan. Not necessarily the best one. Aspia's continuous record means the field information is always current.Updated every satellite flyover, regardless of cloud cover.

You audit your suppliers. You assess their practices and certifications. You've never had an independent measure of the land producing what you buy. Which suppliers are on proven, stable land that performs through difficult seasons. Which are on land that underperforms when conditions turn. Aspia classifies every supplier's land from 10 years of continuous observation. The data exists before the contract is written.

Minder zorgen bij het onboarden van studenten? Ontdek het digitaal studentenattest en elimineer fouten uit je proces!

Poor address capture can cost retailers in two ways: delivery problems when the address is incomplete or inaccurate, and higher cart abandonment rates when adding the address at the checkout is too cumbersome. Our latest blog breaks down what that actually costs in 2026, and where it starts. https://lnkd.in/eixQgUdf

Laravel Cloud now runs AutoMQ with Grafana Labs Mimir 3.0 to power its next-generation observability platform. Here is the story from Florian Beer, Engineering Team Lead, SRE at Laravel. As Laravel Cloud grew, its observability platform had to cover platform services, customer workloads, alerting, and incident response across clusters and nodes worldwide. With hundreds of thousands of metric samples arriving every second, the write path needed to scale without turning Kafka broker disks into the next bottleneck. In the article, Laravel shares how it built that path on AWS with Grafana Mimir 3.0 and AutoMQ: 1️⃣ Mimir ingest storage lets writes land in Kafka before ingesters consume them, so entry writes and backend consumption can scale on separate paths. 2️⃣ A traditional Kafka layer would still bring replicated EBS capacity, broker-to-broker cross-AZ traffic, local log recovery, partition movement, and replica catch-up into the middle of the observability stack. 3️⃣ AutoMQ keeps the Kafka-compatible interface Mimir expects while storing Topic data in S3, so the Kafka buffer follows the same object-storage direction as Mimir long-term blocks. After rollout, Laravel Cloud's metrics volume grew by roughly an order of magnitude, while the entry write path, AutoMQ, and ingester consumption side could still scale independently. 👉 Read the full blog: https://lnkd.in/gJHJhfKT For more Kafka and AutoMQ engineering insights, follow AutoMQ on LinkedIn or join the AutoMQ Slack community: https://lnkd.in/gk8txSCU #GrafanaMimir #LaravelCloud #Kafka #AutoMQ #DisklessKafka #Observability #AWS

One Kafka cost line I think more teams should check: Data Transfer. If you're running Kafka or MSK across multiple AZs on AWS, some of the most expensive traffic may not show up as a “Kafka” or “MSK” cost at all. It is usually buried under EC2-Other / Data Transfer, mixed with network charges from other services. For a 100 MiB/s Kafka cluster with replication factor 3 and three consumer groups, cross-AZ traffic can add roughly $14K-$24K per month, depending on the read path optimizations in place. Where does it come from? 1️⃣ Producers writing to leaders in another AZ 2️⃣ Inter-broker replication across AZs 3️⃣ Consumers reading from brokers in another AZ Some tuning helps, especially for reads. But the replication traffic is architectural. Traditional Kafka stores replicas on brokers, so multi-AZ durability means broker-to-broker data movement. That is the part AutoMQ removes with Diskless Kafka: data persistence moves to object storage, brokers become stateless compute, and cross-AZ replication traffic is eliminated at the root. Full breakdown: https://lnkd.in/dSwGqUci #Kafka #ApacheKafka #AWS #MSK #CloudCosts #DataEngineering #AutoMQ

Rising Monarch costs or support issues making you reconsider? Your existing models may not need rebuilding. See how teams are switching to ReportMiner.