Kai Waehner %

Case Study: From a Monolith to Cloud, Containers, Microservices

ByKai Waehner
24. February 2017

Case Study: How to Move from a (Middleware) Monolith to Cloud, Containers and Microservices leveraging Docker, Cloud Foundry, Kubernetes, Consul, Hystrix, API Management, and others cool things.

ByKai Waehner
24. February 2017

Blockchain, Integration, Streaming Analytics, Ethereum, Hyperledger => Why blockchain is the next big thing for middleware and analytics.

ByKai Waehner
13. February 2017

Data Preparation: Comparison of Programming Languages, Frameworks and Tools for Data Preprocessing and (Inline) Data Wrangling in Machine Learning / Deep Learning Projects.

ByKai Waehner
15. November 2016

Streaming Analytics Comparison of Open Source Frameworks, Products and Cloud Services. Includes Apache Storm, Flink, Spark, TIBCO, IBM, AWS Kinesis, Striim, Zoomdata, …

ByKai Waehner
3. November 2016

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)

ByKai Waehner
3. November 2016

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.

ByKai Waehner
20. October 2016

Build intelligent Microservices by applying Machine Learning and Advanced Analytics. Leverage Apache Hadoop / Spark with Visual Analytics and Stream Processing.

ByKai Waehner
20. October 2016

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.

ByKai Waehner
23. September 2016

JavaOne 2016 Trends: Besides focus on Java platform updates (Java 9, Java EE 8, etc.), I saw three hot topics, which are highly related to each other: Microservices, Docker and Cloud. I also talked about this topic from a middleware perspective. See my slides and lessons learned.

ByKai Waehner
14. September 2016

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.

Technology Evangelist

Kai Waehner

Blockchain, Integration, Streaming Analytics, Ethereum, Hyperledger

Comparison: Data Preparation vs. Inline Data Wrangling in Machine Learning and Deep Learning Projects

Streaming Analytics Comparison of Open Source Frameworks, Products, Cloud Services

Global Field CTO

Apache Kafka vs. Middleware (MQ, ETL, ESB) – Slides + Video

Deep Learning Example: Apache Kafka + Python + Keras + TensorFlow + Deeplearning4j

Demo Title