Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers. This blog post explores why a single real-time pipeline, called Kappa architecture, is the better fit. Real-world examples from companies such as Disney, Shopify, and Uber explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for Lambda.
This blog series explores use cases and architectures for Apache Kafka in the cybersecurity space, including situational awareness, threat intelligence, forensics, air-gapped and zero trust environments, and SIEM / SOAR modernization. This post is part three: Cyber Threat Intelligence.
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.
Apache Kafka Streams to build Real Time Streaming Microservices. Apply Machine Learning / Deep Learning using Spark, TensorFlow, H2O.ai, etc. to add AI. Embed Kafka Streams into Java App, Docker, Kubernetes, Mesos, anything else.
After three great years at TIBCO Software, I move back to open source and join Confluent, the company behind the open source project Apache Kafka to build mission-critical, scalable infrastructures for messaging, integration and stream processsing. In this blog post, I want to share why I see the future for middleware and big data analytics in open source technologies, why I really like Confluent, what I will focus on in the next months, and why I am so excited about this next step in my career.
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