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Geen generieke voorbeelddata of standaardcases die weinig zeggen over jouw situatie en dagelijkse praktijk. Wij bouwen binnen 72 uur een dashboard met jouw eigen data, waarna je het twee weken kunt testen. Seeing-is-Believing.

Maandelijks handmatig rapportages uit 5 systemen? Dat kan slimmer en efficiënter. In 30 min laten we zien hoe al je data samenkomt in één dashboard, zodat je sneller inzicht hebt, minder handmatig werk en áltijd kunt sturen op actuele cijfers.

Stop blaming AI. Start fixing your data. Most AI failures don't happen because the model is bad—they happen because the data is. If your data isn't trusted, your AI isn't either. ✅ Validate every pipeline. ✅ Catch issues before they reach production. ✅ Feed AI with data it can trust. Trusted data isn't optional. It's the foundation of every successful AI initiative. Build AI with confidence. Start with trusted, AI-ready data.

Raw docket records are only the starting point. Datamam collects court data, matches records, normalizes changing source structures, and delivers a clean dataset ready for your systems and analysis.

Monitor case filings, outcomes, dispositions, and county-level trends across selected court sources. Datamam structures the records into a consistent dataset for research, analytics, risk, and legal intelligence teams.

𝗥𝗲𝗽𝗼𝗿𝘁: 𝗖𝗵𝗶𝗻𝗮 𝗡𝗲𝗮𝗿𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗼𝗳 𝗙𝗶𝗿𝘀𝘁 𝗖𝗼𝗺𝗺𝗲𝗿𝗰𝗶𝗮𝗹 𝗧𝘂𝗿𝗯𝗼𝗷𝗲𝘁 𝗘𝗻𝗴𝗶𝗻𝗲 China has been doggedly building out its aerospace sector for decades, seeing steady progress but limited breakthroughs. However, the Chinese aerospace sector may be on the verge of completing a long-stand goal: a reliable, advanced turbojet engine. The CJ-1000A, AECC's turbofan for the COMAC C919, is reportedly closing in on certification, signaling confidence in its reliability under extreme conditions. When completed, China will join a select group of countries that can build an advanced commercial jet engine reliable enough for extended use with limited maintenance. Obtaining this technology has been a long road for China. For decades Russia was China’s top choice for licensed jet engine technology. However, China may have reached the limits of this partnership, putting a ceiling on their development until recently. The biggest barrier thus far has been heat. Modern turbines run hotter than most advanced metal alloys can survive, causing the engine to overheat and destroy itself. However, China has been pouring resources into developing Ceramic Matrix Composites (CMCs), alloys able to handle the extreme conditions that would destroy traditional components. To better understand how China achieved this breakthrough, Datenna dug into our information on CMCs and China’s scientific ecosystem: • Research funding into CMC core materials accelerated in the early 2010s, cresting around 2013 and remaining high. This endeavor was backed by NSFC, MOST and provincial funds • The top research contributors are military-affiliated: Harbin Institute of Technology, Northwestern Polytechnical University, and the PLA’s National University of Defense Technology • Over 80% of jet-engine-related CMC patents were filed after 2016, indicating a sharp increase in capabilities and interest • Procurement climbed sharply in the early 2020s. AECC formed the largest cluster in aerospace propulsion and materials, and tendered the biggest contract on record: a $23M CMC research facility in 2022 Although not yet complete, the certification of China’s advanced jet engine would herald a new arrival in a limited and exclusive clique. China’s entrance into other advanced fields has ushered in disruption and adjustment, and if it can complete this new engine, the aerospace field may feel the reverberations for years to come. Read the full report here: https://lnkd.in/e9EA3K-U/?utm_campaign=Report:Hunter-GathererSemiconductor2026%257CImage%252BVideo%257CGlobal&utm_source=linkedin&utm_medium=paid&utm_id=1153103554&hsa_acc=514863577&hsa_cam=1153103554&hsa_grp=839608014&hsa_ad=1573415424&hsa_net=linkedin&hsa_ver=3 #China #Aerospace #OSINT #MCF

You don’t have time for 200‑page studies but you still need depth. This concise 7‑page intelligence briefing walks you through CJ‑1000A capabilities, key reverse‑engineering milestones, and how silicon‑carbide CMCs moved from lab to procurement. Download the full report to get a clear, data‑driven overview you can share with decision‑makers today.

Quand avez-vous vérifié la composition de votre panel pour la dernière fois ? 👀 Les quotas se remplissent. Le problème se loge ailleurs : une catégorie sous-représentée que personne ne voit, jusqu'à ce que votre client conteste l'étude. Inutile d'interroger plus de monde. Il faut corriger la composition de l'échantillon, dès sa construction. Nous avons accompagné plusieurs instituts de sondage dans cette démarche, sans complexifier leur méthodologie terrain. Votre prochain échantillon mérite le même contrôle. ✅ Notre checklist détaille les 9 points à contrôler. Téléchargez-la maintenant.

See you in Vegas at dbt Summit this week! 🎰 No need to gamble on your migration. We're bringing the fastest migrations to dbt Labs. Come find us, grab a fresh coconut, and let's talk migrations. 📍Datafold Booth #311

The Center for Opportunity and Wealth Data provides comprehensive wealth data to inform policy, investment, research, and decision-making. The Local Wealth Explorer covers 50 U.S. cities - including cities across Congressional Black Caucus districts. Explore census-tract-level insights on assets and debt, business ownership, homeownership, and more. Plus 100+ datasets from trusted public and private sources. Create a free account for full access.

The layer where Spark waste shows up is often not the layer where you need to fix it. High compute utilization, long stages, spill, or expensive shuffles are useful signals. But they can easily lead you toward an obvious optimization – and the wrong one. In one case, I was investigating an expensive KNN workload with high compute utilization and a ~10K-line query plan. Runtime cardinality showed a long-running stage spending half its time sorting ~13B intermediate records – most of which were discarded later in the plan. The fix wasn’t compute tuning. It was changing the application logic to reduce the data earlier in the execution. 𝗦𝗼 𝗵𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗴𝗲𝘁 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘀𝘆𝗺𝗽𝘁𝗼𝗺 𝘁𝗼 𝘁𝗵𝗲 𝗮𝗰𝘁𝘂𝗮𝗹 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻? Trace the execution across infrastructure, Spark runtime, data access, and the physical query plan. Look at executor allocation, task distribution, I/O, shuffle and spill, and operator-level cardinality. The goal is to follow where the work is created, where it accumulates, and where it can actually be eliminated. Roy Daniel and I wrote about this framework and four real production cases in 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝗦𝗽𝗮𝗿𝗸 𝗪𝗮𝘀𝘁𝗲 𝗔𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲 𝗦𝘁𝗮𝗰𝗸, now on the Data Engineer Things blog.

Se connecter à NexSIS, c'est un début. Connecter tout son SI, c'est l'objectif. Avec l'échéance NexSIS, beaucoup de SDIS modernisent leurs échanges avec la plateforme nationale. C'est essentiel — mais c'est une première marche. Car un SDIS, ce n'est pas un système isolé : c'est un écosystème d'applications métiers (RH, indemnisation, décisionnel, référentiels agents…) qui doivent, elles aussi, dialoguer. Standardiser les échanges inter-SDIS, c'est bien. Assurer les échanges avec l'ensemble des parties prenantes et briques internes, c'est aller au bout de la logique. C'est précisément le rôle d'un socle d'interopérabilité (ESB) : 🔹 Décloisonner les applications d'un SI souvent hétérogène 🔹 Fiabiliser et transformer les données entre systèmes 🔹 Industrialiser les échanges plutôt que multiplier les traitements manuels 🔹 Préparer une véritable urbanisation, souple et évolutive, pour les usages de demain Les organisations les plus avancées ne se contentent pas de « brancher » une nouvelle plateforme : elles posent les fondations d'un SI durable, où chaque donnée circule au bon endroit, au bon moment. Parce que l'interopérabilité n'est pas une contrainte réglementaire : c'est un investissement dans la résilience. 📍 Retrouvez l'équipe Blueway Software / SoftProject au Mondial Secours Protection 2026 Haute-Savoie Mont-Blanc Alexis de Saint JeanStyven BARRAQUANDRaphaël BARRAT Cyril Blain #RéponseAuxUrgences #SapeursPompiers #SécuritéCivile #Interopérabilité #Urbanisation #OpérationsConnectées

Gen Z targeting strategies based on assumptions are holding you back. Download the free report and find out where the label helps, where it hurts, and five rules to get it right 👇

The Gen Z label your strategy runs on? It might be holding you back. Download the free report and find out where the label helps, where it hurts, and five rules to get it right 👇

Every market researcher sits somewhere on the AI curve. Most don’t know where. Take the 2-minute quiz to find out where your workflow stands.

Build a funnel from events you're already capturing. No upfront instrumentation, historical data from the moment you create it. Then read the stability, performance and feedback issues behind every drop-off step. Ola Diaa builds one live on Oct 6 and walks from "users are dropping here" to "here's what caused it."

Product sees a conversion drop. Engineering sees a healthy app. The user is stuck in between and nobody owns the step where they left. On Oct 6, Ola Diaa shows how Luciq Funnels puts the funnel chart and the crash dashboard in one view: the crashes, ANRs, network failures and user feedback sitting behind each drop-off step. Live, 45 min. Bring a funnel from your own app for the Q&A.

Incorta connects directly to your SAP & Oracle ERP - delivering live manufacturing insights without the ETL headache.

Manual SAP pulls. Delayed sales reports. Inventory decisions made on last week's numbers. CompositeOne fixed it with one governed data foundation — real-time SKU visibility across 50 warehouses, no new systems required. That's not an inventory problem. It's a data foundation problem.

The Local Wealth Explorer from the Center for Opportunity and Wealth Data gives economic development professionals and CDFIs free access to interactive city maps across 50 U.S. cities. Search by zip code. Explore census-tract-level insights. Identify communities with strong investment potential using our Business Investment Index. Plus, access 100+ datasets from trusted public and private sources. Free with signup.

“AI isn’t coming for the hard part of data engineering. It’s coming for the boring part.” That’s the starting point for Data Basecamp Denver on October 1. If agents handle the plumbing, which half of the job goes to them, and what’s left that matters more than ever? Our three speakers each take a piece of that: 1️⃣ Ben Rogojan on the shift from writing pipelines to directing the systems that write them. The skill isn’t disappearing. It’s moving up a level. 2️⃣ Dan Dorazio on a real migration at Kin that AI moved from stalled to shipped, including the moments where it needed a human to step back in. 3️⃣ Nathan Erbe on why none of this holds up without governance: the data security, shared definitions, and context that keep AI output trustworthy. You’ll leave with things you can use on Monday, not predictions about 2030. Free, in-person, and deliberately small. Thursday, October 1, 5:30 to 8:00 PM MDT at Banshee House in Denver. #DataEngineering #Denver

Harmonic is the startup discovery engine that helps you discover emerging companies, technologies, and markets.

Join us and the Data Engineer Things community for a practical webinar on how to investigate and fix Spark waste across the stack, with a framework and real production cases.

Discover how to bring cloud-native agility and total enterprise control directly to your data, anywhere.