How to do Error Handling in Data Streaming
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Error Handling via Dead Letter Queue in Apache Kafka

Recognizing and handling errors is essential for any reliable data streaming pipeline. This blog post explores best practices for implementing error handling using a Dead Letter Queue in Apache Kafka infrastructure. The options include a custom implementation, Kafka Streams, Kafka Connect, the Spring framework, and the Parallel Consumer. Real-world case studies show how Uber, CrowdStrike, and Santander Bank build reliable real-time error handling at an extreme scale.
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Data Streaming with Apache Kafka in the Healthcare Industry
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Apache Kafka in the Healthcare Industry

IT modernization and innovative new technologies change the healthcare industry significantly. This blog series explores real-world examples of data streaming with Apache Kafka to increase efficiency, reduce cost, and improve the human experience across the healthcare value chain including pharma, insurance, providers, retail, and manufacturing. This is part one: Overview.
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The Trinity of Data Streaming in Industrial IoT - Apache Kafka MQTT OPC UA
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OPC UA, MQTT, and Apache Kafka – The Trinity of Data Streaming in IoT

In the IoT world, MQTT and OPC UA have established themselves as open and platform-independent standards for data exchange in Industrial IoT and Industry 4.0 use cases. Data Streaming with Apache Kafka is the data hub for integrating and processing massive volumes of data at any scale in real-time. This blog post explores the relationship between Kafka and the IoT protocols, when to use which technology, and why sometimes HTTP/REST is the better choice. The end explores real-world case studies from Audi and BMW.
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Apache Camel vs Apache Kafka Comparison
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When to use Apache Camel vs. Apache Kafka?

Should I use Apache Camel or Apache Kafka for my next integration project? The question is very valid and comes up regularly. This blog post explores both open-source frameworks and explains the difference between application integration and event streaming. The comparison discusses when to use Kafka or Camel, when to combine them, when not to use them at all. A decision tree shows how you can quickly qualify out one for the other.
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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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Is Apache Kafka an iPaaS or is Event Streaming its own Software Category?

This post explores why Apache Kafka is the new black for integration projects, how Kafka fits into the discussion around cloud-native iPaaS solutions, and why event streaming is a new software category. A concrete real-world example shows the difference between event streaming and traditional integration platforms respectively iPaaS.
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Apache Kafka Hard vs Soft Real Time for Industrial IoT Robots and Connected Vehicles
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Apache Kafka is NOT Hard Real Time BUT Used Everywhere in Automotive and Industrial IoT

Apache Kafka is NOT hard real-time in Industrial IoT or vehicles (such as autonomous cars) but integrates the OT/IT world for near real-time data correlation and analytics in hybrid architectures across factories at the edge, multiple clouds, and over countries.
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Postmodern ERP with Apache Kafka
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Building a Postmodern ERP with Apache Kafka

Postmodern ERP represents the next generation of ERP architectures. It is real-time, scalable, and open by using a combination of open source technologies and proprietary standard software. This blog post explores why and how companies, both software vendors and end-users, leverage event streaming with Apache Kafka to implement a Postmodern ERP.
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