This post shares a slide deck and video recording of the differences between an event-driven streaming platform like Apache Kafka and middleware like Message Queues (MQ), Extract-Transform-Load (ETL) and Enterprise Service Bus (ESB).
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.
Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural…
Streaming Analytics Comparison of Open Source Frameworks, Products and Cloud Services. Includes Apache Storm, Flink, Spark, TIBCO, IBM, AWS Kinesis, Striim, Zoomdata, …
Comparison of Open Source IoT Integration Frameworks such as Eclipse Kura (+ Apache Camel), Node-RED, Flogo, Apache Nifi, StreamSets, and others… (slide and video recording)
Introduction to the Open Source IoT Integration Project Flogo to build very lightweight edge applications and microservices for cloud native containers / cloud platforms / serverless architectures.
Build intelligent Microservices by applying Machine Learning and Advanced Analytics. Leverage Apache Hadoop / Spark with Visual Analytics and Stream Processing.
Log Analytics is the right framework or tool to monitor for Distributed Microservices. Comparison of Open source, SaaS and Enteprrise Products. Plus relation to big data components such as Apache Hadoop / Spark.
Closed Big Data Loop: 1) Finding Insights with R, H20, Apache Spark MLlib, PMML and TIBCO Spotfire. 2) Putting Analytic Models into Action via Event Processing and Streaming Analytics.
I was invited to speak at Microservices Meetup Dublin this week. I updated my slide deck “Microservices – Death of the ESB?”