A practical guide: Real-time EDI Processing over SFTP Using Webhooks #EDI #SFTP #Webhooks #RealTimeProcessing #FileAutomation #B2BIntegration #DataIntegration #SecureFileTransfer #EventDrivenArchitecture #SFTPToGo #WorkflowAutomation #EDIProcessing sftptogo.com/blog/real-time-…

📢#CallForReading | #Article in Automation Embedded Implementation of Real-Time Voice Command Recognition on #PIC Microcontroller 🧑‍By Mohamed Shili, Salah Hammedi, Amjad Gawanmeh & Khaled Nouri 🔗Full Paper: mdpi.com/2673-4052/6/4/79 #VoiceRecognition #RealTimeProcessing

A Symmetric Multiprocessor System-on-a-Chip-Based Solution for Real-Time Image Dehazing ✏️ Dat Ngo and Bongsoon Kang 🔗 brnw.ch/21x0qMH Viewed: 1645; Cited: 8 #mdpisymmetry #MPSoC #imagedehazing #deeplearning #realtimeprocessing @ComSciMath_Mdpi

How and Why Netflix Built a Real-Time Distributed Graph Ingesting and Processing Data Streams at Internet Scale, Building a Scalable Storage Layer The Netflix product experience historically consisted of a single core offering: streaming video on demand. But the evolution of its business has created a new class of problems where member interactions with the app have to be analyzed across different business verticals. In a traditional data warehouse, these events would land in at least two different tables and may be processed at different cadences. But in a graph system, they become connected almost instantly. Ultimately, analyzing member interactions in the app across domains empowers Netflix to create more personalized and engaging experiences. The data engineering team recognized a solution to process and store swathes of interconnected data while enabling fast querying to discover insights is needed. Although they could have structured the data in various ways, they ultimately settled on a graph representation. Graph offers key advantages, specifically: * Relationship-Centric Queries * Flexibility as Relationships Grow * Pattern and Anomaly Detection This is why they set out to build a Real-Time Distributed Graph, or “RDG” for short. Three main layers in the system power the RDG: * Ingestion and Processing — receive events from disparate upstream data sources and use them to generate graph nodes and edges. * Storage — write nodes and edges to persistent data stores. * Serving — expose ways for internal clients to query graph nodes and edges. The team built the ingestion and processing pipeline using Apache Flink to transform streaming events into graph primitives. But the critical question is - once billions of nodes and edges are created from member interactions, how do you actually store them? The RDG is a property graph consisting of: * Nodes: Entities including member accounts, titles (such as shows/movies), devices, and games. Each node has a unique identifier and a set of properties containing additional metadata. * Edges: Relationships between nodes, such as “started watching,” “logged in from,” or “plays.” Edges also have unique identifiers and properties, such as timestamps. In evaluating different storage options, Netflix explored traditional graph datastores. While they do provide feature-rich capabilities around things like native-graph query support and data models to represent different types of graphs, they also pose a mix of scalability, workload, and ecosystem challenges. They ultimately decided that the options evaluated wouldn’t meet requirements at Netflix’s scale. So they turned instead to an internal platform specifically designed for this type of challenge: the Data Gateway Platform. More specifically, its Key-Value Data Abstraction Layer (KVDAL). For the RDG, Netflix provisions a separate namespace for every node type and edge type in the graph. This also makes it straightforward to extend the RDG with new types of nodes and edges. By Adrian Taruc, James Dalton, Luis Medina, Ajit Koti How and Why Netflix Built a Real-Time Distributed Graph: Part 1 — Ingesting and Processing Data Streams at Internet Scale netflixtechblog.com/how-and-… How and Why Netflix Built a Real-Time Distributed Graph: Part 2 — Building a Scalable Storage Layer netflixtechblog.medium.com/h… #EmergingTech #DataEngineering #ConnectedData #RealTimeProcessing -- 📩 The Year of the Graph Winter 2025-2026 newsletter issue is out! The Ontology issue: From knowledge to graphs and back again 👇 yearofthegraph.xyz/newslette… All things #KnowledgeGraph, #GraphDB, Graph #Analytics / #DataScience / #AI and #SemTech. Subscribe and follow to be in the know. Reach out if you'd like to be featured

MAPS Messaging delivers sub-20ms latency, 40% faster local decisions & 30% less cloud data—thanks to intelligent MQTT-CoAP bridging. See how it transforms your edge strategy: mapsmessaging.io! #EdgeIntelligence #ProtocolBridging #SmartEdge #RealTimeProcessing #IoTSolutions

시장이 흔들릴 때, 우리가 붙잡아야 할 것 [@River4fun @RiverdotInc ] 요즘 차트만 보면 심장이 먼저 반응합니다. 빨갛게 번지는 캔들 사이에서 손가락이 먼저 매도 버튼을 찾게 됩니다. 그럴 때마다 전 워런 버핏의 한 문장을 떠올린다. “남들이 탐욕을 보일 때 두려워하고, 남들이 두려워할 때 탐욕을 가져라.” 이 말은 타이밍 잡아 ‘올인’하라는 주문이 아니고 공포가 짙을수록 우리가 이길 확률이 높다는 뜻이죠. 시장이 소음을 키울수록, 우리는 규칙을 정립해야 합니다. 지금 당장 써먹는 5가지 버티기 규칙 시간 프레임 분리: “단타 계정 vs 장기 계정”을 물리적으로 분리. 장기 계정은 알림 꺼두고, 분기별로만 점검. 현금흐름 만들기: 소액 DCA(정액분할) 일정 고정—날짜가 판단을 대신하게 한다. 시장 기분보다 달력이 더 강해지게. 리스크 캡: 한 포지션 손실 한도(예: 원금의 1–2%)를 미리 정해두고 자동화. 규칙이 감정보다 빨라야 한다. 체크리스트 3줄: “논리(왜 사는가) · 조건(언제 틀렸다고 인정할까) · 기간(얼마나 버틸까)”. 이 세 줄이 있으면 폭락장에서 흔들림이 줄어든다. 성과지표 바꾸기: 단기 PnL 대신 준수율(내 규칙을 지킨 비율), 오류감소(같은 실수 재발률), 현금보유율을 주간 성적표로 본다. 그리고 또 하나, 버핏의 이 말도 잊지 맙시다. “시장은 조급한 이에게서 인내심 있는 이에게 돈을 옮기는 장치다.” 코인 시장은 유난히 소란스럽습니다. 하락을 하고 밸런서에서 10억 달러 넘는 금액이 해킹을 당하고 그 소란 속에서도 우리는 속도 대신 지속, 소문 대신 원칙, 예감 대신 데이터를 붙잡아 봅시다. 오늘의 하락은 내일의 서사에선 한 페이지일 뿐이고 당장 완벽해지려 하지 말자. 오늘은 규칙 하나만 더 지켜봅시다. 그게 버티는 힘이고, 끝내는 이기는 방법입니다. 그래서 결론은 뭐다,, 야핑을 하고 폐지를 주웁시다. 없토버니 업토버니 의미없다. 의미없어 리버 핵심 키워드 • Omni-CDP (Omnichain Collateralized Debt Position) • satUSD (Omni-CDP 기반 스테이블코인) • Chain Abstraction (체인 추상화) • DeAI Integration (분산형 AI 통합) • Dynamic Airdrop (동적 에어드랍) • Liquidity Layer (유동성 레이어) • Staking & Vault (스테이킹 및 볼트) • Cross-Chain Minting (크로스 체인 민팅) • Bridge-less Multichain (브리지 없는 멀티체인) 포고 핵심 키워드 -새로운 고성능 레이어1 -솔라나 기반의 진화형 L1 등장 -Firedancer 순수 활용으로 극한의 퍼포먼스 #Firedancer #UltraPerformance #RealTimeProcessing

How is edge data processed? Via @antgrasso #EdgeComputing #EdgeData #DataAtTheEdge #RealTimeProcessing #SmartData #EdgeAnalytics #EdgeTech #EdgeIntelligence #FutureOfData cc: @CurieuxExplorer @mvollmer1 @Khulood_Almani

#mdpisymmetry Check this published article "A Symmetric Multiprocessor System-on-a-Chip-Based Solution for Real-Time Image Dehazing" at brnw.ch/21wTqgH Authors: Dat Ngo and Bongsoon Kang #deeplearning #realtimeprocessing #MPSoC @ComSciMath_Mdpi

🚀 Raspberry Pi with OpenCV: Getting Hands-On with AI at the Edge Creating a product isn’t just about software; hardware plays a key role too! In computer vision, we need compact edge hardware to deploy our object detection models. Enter Raspberry Pi—one of the most popular edge devices—and OpenCV, the go-to library for computer vision. 📸 By processing data locally on the edge, Raspberry Pi reduces latency and bandwidth usage, enabling real-time responses in a small form factor. Want to deploy computer vision apps locally? In this article, I’ll guide you through setting up Raspberry Pi and installing OpenCV to create your own local edge environment. 🔧 💡Read the full blog: opencv.org/blog/raspberry-pi… To fully unlock the potential of AI technologies, our Free OpenCV BootCamp is the perfect place to start. ✅ Free OpenCV Bootcamp: opencv.org/university/free-o… ✅ Free Pytorch Bootcamp: opencv.org/university/free-p… ✅ Free TensorFlow Bootcamp: opencv.org/university/free-t… Master AI, Deep Learning, and Computer Vision with our expert-led certification course. Gain hands-on experience, work on real-world projects, and develop industry-ready skills to kickstart or advance your AI career. ➡️Download Curriculum Here: opencv.org/university/cvdl-m… #EdgeComputing #ComputerVision #RaspberryPi #OpenCV #AI #TechTutorial #RealTimeProcessing

Why Businesses Need Both Edge and Cloud Computing for Success - Techiexpert.com #5G #AI #CloudComputing #EdgeComputing #IOT #RealTimeProcessing #Sustainability #TechInnovation #Technology myteam.ericsson.net/9aJgtRyi…

Real-time data processing transforms how we make decisions. By analyzing data on-the-fly, businesses can respond instantly to trends, enhancing efficiency and insights. Embrace the future of data! #DataAnalytics #RealTimeProcessing

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Real-time data processing empowers businesses to make instant decisions and enhance user experiences. Harness the power of data as it streams in to stay ahead of the competition. #DataAnalytics #RealTimeProcessing

The Reality of DePIN: Blockchain Integration or Marketing Gimmick? #DePIN #Blockchain #Crypto #IoT #Ethereum #Solana #MegaETH #AI #Web3 #Tokens #Scalability #Decentralization #WirelessNetworks #Theta #CUDOS #Akash #Sui #AutonomysNetwork #Layer2 #RealTimeProcessing

☁️➡️🖥️ Seamless Cloud-to-Edge Integration aZen Protocol ensures workloads shift seamlessly between cloud, edge, and endpoint, enabling real-time processing at the edge while leveraging the cloud for large-scale analytics. 🎯 Cloud-edge-endpoint integration unlocks new possibilities for computing! #CloudComputing #EdgeComputing #RealTimeProcessing #Web3 #TechInnovation #Blockchain #DataAnalytics #aZenProtocol