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manufacturing

96 posts
Data Streaming with Apache Kafka for Industrial IoT in the Automotive Industry at Brose
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Apache Kafka in Manufacturing at Automotive Supplier Brose for Industrial IoT Use Cases

Data streaming unifies OT/IT workloads by connecting information from sensors, PLCs, robotics and other manufacturing systems at the edge with business applications and the big data analytics world in the cloud. This blog post explores how the global automotive supplier Brose deploys a hybrid industrial IoT architecture using Apache Kafka in combination with Eclipse Kura, OPC-UA, MuleSoft and SAP.
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The State of Data Streaming for Manufacturing in 2023
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The State of Data Streaming for Manufacturing

This blog post explores the state of data streaming for manufacturing. The evolution of industrial IoT, manufacturing 4.0, and digitalized B2B and customer relations require modern, open, and scalable information sharing. The foci are trending enterprise architectures in the manufacturing industry and data streaming customer stories from BMW, Mercedes, Michelin, or Siemens. A complete slide deck and on-demand video recording are included.
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Automotive and Manufacturing Landscape for Apache Kafka
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Apache Kafka Landscape for Automotive and Manufacturing

Apache Kafka is the central nervous system of many applications in various areas related to the automotive and manufacturing industry for processing analytical and transactional data in motion across edge, hybrid, and multi-cloud deployments. This article explores the event streaming landscape for automotive including connected vehicles, smart manufacturing, supply chain optimization, aftersales, mobility services, and innovative new business models.
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Apache Kafka for Industrial IoT and Manufacturing 4.0

This post explores use cases and architectures for processing data in motion with Apache Kafka in Industrial IoT (IIoT) across verticals such as automotive, energy, steel manufacturing, oil&gas, cybersecurity, shipping, logistics. Use cases include predictive maintenance, quality assurance, track and track, real-time locating system (RTLS), asset tracking, customer 360, and more. Examples include BMW, Bosch, Baader, Intel, Porsche, and Devon.
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MQTT and Kafka for Manufacturing and Industrial IoT
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Apache Kafka and MQTT (Part 3 of 5) – Manufacturing 4.0 and Industrial IoT

Apache Kafka and MQTT are a perfect combination for many IoT use cases. This blog series covers the pros and cons of both technologies. Various use cases across industries, including connected vehicles, manufacturing, mobility services, and smart city are explored. The examples use different architectures, including lightweight edge scenarios, hybrid integrations, and serverless cloud solutions. This post is part three: Manufacturing, Industrial IoT, and Industry 4.0.
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Data Streaming with Apache Kafka and Flink in the Cloud in Healthcare and Pharma at Cardinal Health
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Data Streaming in Healthcare and Pharma: Use Cases and Insights from Cardinal Health

This blog delves into Cardinal Health’s journey, exploring how its event-driven architecture and data streaming power use cases like supply chain optimization, and medical device and equipment management. By integrating Apache Kafka with platforms like Apigee, Dell Boomi and SAP, Cardinal Health sets a benchmark for IT modernization and innovation in the healthcare and pharma sectors.
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Real-Time AI ML Model Inference Predictive AI and Generative AI with Data Streaming using Apache Kafka and Flink
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Real-Time Model Inference with Apache Kafka and Flink for Predictive AI and GenAI

Artificial Intelligence (AI) and Machine Learning (ML) are transforming business operations by enabling systems to learn from data and make intelligent decisions for predictive and generative AI use cases. Two essential components of AI/ML are model training and inference. This blog post explores how data streaming with Apache Kafka and Flink enhances the performance and reliability of model predictions. Whether for real-time fraud detection, smart customer service applications or predictive maintenance, understanding the value of data streaming for model inference is crucial for leveraging AI/ML effectively.
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