Apache Kafka became the de facto standard for event streaming. Various vendors added Kafka and related tooling to their offerings or provide a Kafka cloud service. This blog post uses the car analogy – from the motor engine to the self-driving car – to explore the different Kafka offerings available on the market. The goal is not a feature-by-feature comparison. Instead, the intention is to educate about the different deployment models, product strategies, and trade-offs from the available options.
Data integration and processing in Industrial IoT (IIoT, aka Industry 4.0 or Automation Industry). Apache Kafka, its ecosystem (Kafka Connect, KSQL) and Apache PLC4X are a great open source choice to implement this integration end to end in a scalable, reliable and flexible way.
Machine Learning / Deep Learning models can be used in different ways to do predictions. Natively in the application or hosted in a remote model server. Then you combine stream processing with RPC / Request-Response paradigm. This blog post shows examples of stream processing vs. RPC model serving using Java, Apache Kafka, Kafka Streams, gRPC and TensorFlow Serving.
This blog explains the different components of a hybrid integration architecture, their deployment models, when to use them, and their target audience. Afterward, each section shows how TIBCO’s Hybrid Integration Platform maps to this.
This article shows the different components available for a Hybrid Integration Architecture. The goal is not to discuss different vendor offerings but to explain different concepts and benefits of each component in general and how they relate to each other. Including concepts such as Hybrid Integration Platform (HIP), Cloud-Native Middleware, PaaS, Docker, iPaaS, iSaaS, API Management, and others.
This article discusses how relevant microservices, containers and a cloud-native architecture is for middleware. It is unbelievable how fast enterprises of all sizes are moving forward with these topics!
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