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Incidents stall at the handoffs, when each team works from different tools and different data. cPacket AI workflows reduce MTTI and MTTR by giving NetOps, SecOps, and app teams shared visibility into packet-level insights: • Align on root cause faster with common evidence • Skip the handoffs and tool-hopping between teams • Stop finger-pointing with evidence everyone can see One view. Fewer handoffs.

Packet data holds the answer to most network questions, but only a few people on your team can read it. With cPacket AI, engineers, app owners, and L1/L2 staff get clear answers without spreading your senior engineers too thin: 💬 Ask questions in plain language 🔍 Follow the same troubleshooting steps as your senior engineers 🚀 Get answers backed by packet evidence Scale expertise across teams without scaling headcount

Your GPUs are portable. Your storage isn't. Multi-vendor GPU stacks have a silent single-vendor dependency: the storage layer. If the compute moves and the data can't follow, the strategy stops at the data. Akave Cloud is S3-compatible and verifiable sovereign storage, so your data stays portable across environments with $0 egress fees. Drop in your current egress bill and see what Zero egress bill looks like against it.

If your GPUs are portable across vendors and your storage is not, You are locked in wherever the storage lives. The multi-vendor GPU pattern has a silent single-vendor dependency: The storage layer. Akave Cloud provides S3 compatible, verifiable storage layer which reduces single vendor dependency and makes your data portable with $0 egress fees. Don't believe us? Try Akave Cloud Risk free or compare the egress cost to compare how much you save with us?

Ellipsis Drive catalogs spatial data across existing storage, helping teams and applications find and use it without moving source files.

From Target to Costco, Noka gains time back with Crisp EDI – simplifying compliance, boosting visibility, and fueling growth across retail partners.

Simplify EDI & reduce costs: Automate orders with accuracy across retail, pharma, marketplaces, 3PLs, and more. 1,500 partners and full ERP integration. Free up teams for more.

A private field service executive community with zero vendors in it. No vendors trying to sell you. No junior marketers. No upsells. Only your peers in field service.

A private field service executive community with zero vendors in it. No vendors trying to sell you. No junior marketers. No upsells. Only your peers in field service.

When latency spikes, decisions miss their window. In an independent benchmark against Redis, Aerospike matched in-memory latency with far less jitter, even when serving data straight from disk. See the results across four configurations.

Misclassified inventory eating into your CPMs? Need to add device identification to your existing bid-request flow without a rebuild? Your in-house parser can't keep up with the CTV device long tail? Whatever your challenge, bring it to Stand B059a. We'll be at DMEXCO 2026, Hall 06.1, September 23–24 — ready to work through it with you in person. #DMEXCO2026 #CTV #AdTech #Programmatic *Source: DeviceAtlas

AI workloads are growing while agents create demand at machine speed. Get the data on what that means for the infrastructure underneath.

🚀 Bis zu 30.000 € Förderung für den Einstieg in Catena-X Steigende Anforderungen ihrer Kunden, neue Berichtspflichten und fragile Lieferketten stellen viele Unternehmen der Automobilindustrie vor dieselbe Aufgabe: Informationen müssen schnell, sicher und effizient mit Partnern ausgetauscht werden. Das geht mit Catena-X: Der Datenraum ermöglicht Unternehmen, Daten nach gemeinsamen Standards und Regeln über einheitliche Prozesse mit ihren Geschäftspartnern auszutauschen. Das Wichtigste: Sie behalten die Kontrolle und entscheiden, welche Informationen Sie mit welchen Partnern teilen. Die Daten werden weder in einer geteilten Plattform gesammelt noch zentral verwaltet. Typische Anwendungen sind der Austausch von CO₂-Daten, Qualitätsinformationen oder Angaben zu verfügbaren Kapazitäten und Bedarfen. Der Einstieg ist 2026 so leicht wie noch nie: Im Rahmen einer Studie der deutschen Bundesregierung erhalten Unternehmen finanzielle Unterstützung, um einen produktiven Datenaustausch in Catena-X aufzubauen. Je nach Anwendungsfall stehen 15.000 € oder 30.000 € Fördermittel zur Verfügung, um die anfänglichen Implementierungs- und Einstiegskosten abzudecken. Besonders interessant ist das Angebot für: ✅ KMU und Unternehmen, die Catena-X erstmals nutzen möchten ✅ Organisationen mit wenig Erfahrung im Bereich Data Spaces ✅ Teams, die konkrete Anwendungsfälle umsetzen wollen, z. B. Product Carbon Footprint (PCF), Zertifikatsmanagement oder den Digitalen Batteriepass 🏛️ Der Data Space Accelerator wird durch das Bundesministerium für Wirtschaft und Energie gefördert und verfügt über ein Fördervolumen von 23 Millionen Euro. Die Studie läuft ausschließlich im Jahr 2026. Interessiert? 🔗 Mehr erfahren und bewerben: https://lnkd.in/exaXSX-a

Up to €30,000 in funding to get started with Catena-X 🚘 Automakers are increasingly requiring their upstream partners to participate in Catena-X. For some OEMs, data exchange on Catena-X is even already a requirement in their tender conditions. In particular, the exchange of company certificates and Product Carbon Footprints (PCF) is increasingly being required. In 2026, a government-funded study will support companies in getting started: 💶 €15,000 in funding for Company Certificate Management (CCM) or €30,000 for CCM and PCF ✅ Support with initial setup and implementation costs ✅ Particularly interesting for companies looking to implement their first Catena-X use cases 🏛️ The Data Space Accelerator is funded by the Federal Ministry for Economic Affairs and Energy and has a total funding volume of 23 million euros. This funding is available exclusively in 2026. If you’re planning to get started with Catena-X, now is a good time to take the next step. 🔗 Learn more and apply: https://lnkd.in/eRz6K2rv

Sua empresa sabe quem está por trás de cada cadastro? Informações incompletas dificultam a identificação de riscos e podem expor sua operação a fraudes. Com a Datahub, sua equipe conta com inteligência de dados para analisar perfis e comportamentos, fortalecer o KYC e apoiar as rotinas de compliance. Conheça melhor seus clientes e tenha mais informação para decidir com segurança. Clique em Saiba mais e descubra como a Datahub pode apoiar sua operação.

Sua equipe tem informações suficientes para aprovar um cliente ou parceiro com segurança? Cadastros incompletos dificultam a validação e podem deixar riscos passarem despercebidos. A Datahub fornece dados para conhecer melhor pessoas e empresas, fortalecer processos de KYC e KYB e apoiar sua rotina de compliance. Tenha mais informação para validar com critério e decidir com confiança. Clique em Saiba mais e conheça as soluções da Datahub para sua operação.

Doing nothing costs money, but spending money makes more money. Such is the paradox of in-app analytics. But that's only the first paradox. There are two, and both show up for just about every product engineering team that starts looking into an in-app analytics module. If you work in B2B SaaS and your customers ask for dashboards, you know the first one well, and you're almost certainly living the second. The plot is as predictable as Campbell's hero's journey: 1/ You're a small team building B2B SaaS. Fewer than 100 people, serving, let's say, around 1,500 customers, SMBs and enterprises both. 2/ Your Customer Success team starts getting the same kind of request over and over: data. At first you point them to the export button. 3/ Then they ask whether they can slice and dice it. Filter the numbers down to one product, compare them across time periods. 4/ Now Customer Success is buried in data requests they aren't equipped to handle. They take it to product engineering, who have their own fires to put out. 5/ Product weighs buy against build and, like any proud product engineering team, decides it can't be that hard to build. 6/ It turns out it isn't too hard, but it just takes A LOT of time to get it right (not looking like a random dashboard bolted onto your UI, row-level permissions, cross-filter and drills), time you could have spent on the core features of your product. 7/ So it drags on, neither here nor there. You ship a half-baked analytics module while the rest of your roadmap gathers cobwebs, and customers start threatening to leave for your competitors. The money is on the table, and it should have been collected much much sooner. 8/ Customer Success, meanwhile, is stuck between two teams: passing analytics requests to product, and still bugging engineering or the data team for one-off dashboards. Which leads us to the second paradox. It's this: if you want to know how well your in-app analytics is performing, you don't just ask product or data. You ask Customer Success too. Because when the analytics is doing its job, a whole category of message goes missing: the feature requests, the "can you pull this for me" asks, the support threads that used to stack up high. They're simply gone. What replaces them is a far easier ask, and a better one: “more data, please”. And, “Where do I upgrade to the premium plan with the analytics?” That's how it went for our customers. It can go that way for you too, with Holistics.

(This is how data teams are buying BI tools: see the screenshots) Half the data teams we spoke with over the last three weeks asked about the same set of questions. The wording varies, but it's always some version of: 1/ Do you have a semantic layer? 2/ How expressive is your semantic layer? 3/ Do you have a code-first development flow? And in most cases, our answer is the same: Yes. Let me show you. Yes. And they're skeptical of that answer, as any good data practitioner should be. They know all too well how B2B vendors over-promise and under-deliver. They know how easy it is to bolt a natural language interface onto legacy BI and call it AI analytics. Stuck between hearing the same AI pitch for the hundredth time and fielding the hundredth Slack DM asking for a quick metric pull, they've gotten good at filtering out options fast. So they ask us what they need to ask. They ask about the semantic layer to ground AI in shared metric definitions. They ask about a code-based development flow so they can govern and version-control it, the way they would in software engineering. And they ask about expressiveness because they want one that can also handle the harder questions, like nested aggregation or period-over-period analysis, when most semantic layers handle the OLAP basics (lookup, slice-and-dice, filter/group, top-N). They already know that if the layer can't express a question natively, AI either falls back to raw SQL (bringing back hallucination risk) or just returns nothing. They ask what they need to ask. And these are good, important questions, because without them, they'd never find and try out Holistics, and they'd never tell us what they told us: "Holistics AI is surprisingly good, even when we hadn't done much work contextualizing our data." -- P.S. Like any good data practitioner, you should be skeptical of this post too. But if you want to see how we win over even the biggest skeptics, book a demo with us.

A valid login proves who’s requesting data – not what it should access. The Data Authorization Layer makes that decision.

Your agent can log in and still reach the wrong data. The Data Authorization Layer decides what the request can access and why.

Join data visionaries, Cloudera experts, and customers at EVOLVE26, the industry’s must-attend Data and AI Conference.

EVOLVE26 is the industry’s biggest Data & AI event. See how leading organizations are turning AI into reality and hear from the visionaries behind the next wave of game-changing AI innovation.

Coupler.io covers 400+ sources on one plan — ads, CRM, ecommerce, finance, email. and more. Connect the accounts, pick the columns, set the refresh. One connector to run, one bill at renewal, no per-destination fee.

Coupler.io loads ads, ecommerce, finance and CRM into BigQuery as ready-to-use tables, refreshed daily. Every report reads one source, so the numbers stop disagreeing. No SQL. No code. Start free — 7 days, no card.