Data Streaming with Apache Kafka at Airlines - Lufthansa Case Study
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How Lufthansa uses Apache Kafka for Middleware and Analytics

Aviation and travel are notoriously vulnerable to social, economic, and political events, as well as the ever-changing expectations of consumers. The coronavirus was just a piece of the challenge. This post explores how Lufthansa leverages data streaming powered by Apache Kafka as cloud-native middleware for mission-critical data integration projects and as data fabric for AI/machine learning scenarios such as real-time predictions in fleet management. An interactive conversation with Lufthansa as an on-demand video is added at the end as a highlight if you want to learn more.
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Event Streaming as the Data Hub for Crypto NFT and Metaverse
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Apache Kafka as Data Hub for Crypto, DeFi, NFT, Metaverse – Beyond the Buzz

Decentralized finance with crypto and NFTs is a huge topic these days. It becomes a powerful combination with the coming metaverse platforms across industries. This blog post explores the relationship between crypto technologies and modern enterprise architecture. I discuss how event streaming and Apache Kafka help build innovation and scalable real-time applications of a future metaverse. Let’s skip the buzz (and NFT bubble) and instead review existing real-world deployments in the crypto and blockchain world powered by Kafka and its ecosystem.
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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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Stream Exchange for Data Sharing with Apache Kafka in a Data Mesh
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Streaming Data Exchange with Kafka and a Data Mesh in Motion

Data Mesh is a new architecture paradigm that gets a lot of buzzes these days. This blog post looks into this principle deeper to explore why no single technology is the perfect fit to build a  Data Mesh. Examples show why an open and scalable decentralized real-time platform like Apache Kafka is often the heart of the Data Mesh infrastructure, complemented by many other data platforms to solve business problems.
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Apache Kafka for Government and Citizen Services in the Public Sector
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Apache Kafka in the Public Sector – Part 3: Government and Citizen Services

The public sector includes many different areas. Some groups leverage cutting-edge technology, like military leverage. Others like the public administration are years or even decades behind. This blog series explores both edges to show how data in motion powered by Apache Kafka adds value for innovative new applications and modernizing legacy IT infrastructures. This is part 3: Use cases and architectures for Government and Citizen Services.
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Innovation in Financial Services and Open Banking with Apache Kafka
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Apache Kafka in the Financial Services Industry

The rise of event streaming in financial services is growing like crazy. Continuous real-time data integration and processing are mandatory for many use cases. Apache Kafka is deployed across the financial services business departments for mission-critical transactional workloads and big data analytics. High scalability, high reliability, and an elastic open infrastructure are the key reasons for the success of Kafka. This blog post explores different use cases, architectures, and real-world examples in the FinServ sector.
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Digital Twin with Apache Kafka - Simulating of car manufacturing by robots on Siemens
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Apache Kafka as Digital Twin for Open, Scalable, Reliable Industrial IoT (IIoT)

This blog post discusses the benefits of a Digital Twin in Industrial IoT (IIoT) and its relation to…
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IoT Live Demo – 100.000 Connected Cars with Kubernetes, Kafka, MQTT, TensorFlow

Live Demo – 100.000 Connected Cars – Real Time Processing and Analytics with Kubernetes, Kafka, MQTT and TensorFlow leveraging Confluent and HiveMQ.
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