Fraud detection becomes increasingly challenging in a digital world across all industries. Real-time data processing with Apache Kafka became the de facto standard to correlate and prevent fraud continuously before it happens. This blog post explores case studies for fraud prevention from companies such as Paypal, Capital One, ING Bank, Grab, and Kakao Games that leverage stream processing technologies like Kafka Streams, KSQL, and Apache Flink.
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.
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, Uber, and Twitter 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.
The combination of Apache Kafka and Machine Learning / Deep Learning are the new black in Banking and…
KSQL – The Open Source Streaming SQL Engine for Apache Kafka => Slides from my talk at Big Data Spain 2018 are online. Check it out!
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 + Kafka Streams + Apache Mesos = Highly Scalable Microservices. Mission-critical deployments via DC/OS and Confluent on premise or public cloud.
Apache Kafka + Kafka Streams to Produductionize Neural Networks (Deep Learning). Models built with TensorFlow, DeepLearning4J, H2O. Slides from JavaOne 2017.