Deep Learning KSQL UDF for Streaming Anomaly Detection of MQTT IoT Sensor Data

Posted in Analytics, Apache Kafka, Big Data, Cloud, Cloud-Native, Confluent, Deep Learning, Integration, Internet of Things, Java / JEE, Kafka Connect, Kafka Streams, KSQL, Machine Learning, Microservices, MQTT, Open Source on August 2nd, 2018 by Kai Wähner

I built a scenario for a hybrid machine learning infrastructure leveraging Apache Kafka as scalable central nervous system. The public cloud is used for training analytic models at extreme scale (e.g. using TensorFlow and TPUs on Google Cloud Platform (GCP) via Google ML Engine. The predictions (i.e. model inference) are executed on premise at the edge in a local Kafka infrastructure (e.g. leveraging Kafka Streams or KSQL for streaming analytics).

This post focuses on the on premise deployment. I created a Github project with a KSQL UDF for sensor analytics. It leverages the new API features of KSQL to build UDF / UDAF functions easily with Java to do continuous stream processing on incoming events.

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Model Serving: Stream Processing vs. RPC / REST with Java, gRPC, Apache Kafka, TensorFlow

Posted in Analytics, Apache Kafka, Big Data, Confluent, Deep Learning, Java / JEE, Kafka Streams, KSQL, Machine Learning, Microservices, Open Source, Stream Processing on July 9th, 2018 by Kai Wähner

Machine Learning / Deep Learning models can be used in different ways to do predictions. My preferred way is to deploy an analytic model directly into a stream processing application (like Kafka Streams or KSQL). You could e.g. use the TensorFlow for Java API. This allows best latency and independence of external services. Several examples can be found in my Github project: Model Inference within Kafka Streams Microservices using TensorFlow, H2O.ai, Deeplearning4j (DL4J).

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Deep Learning at Extreme Scale 
with the Apache Kafka Open Source Ecosystem

Posted in Analytics, Apache Kafka, Big Data, Cloud, Confluent, Deep Learning, Integration, Kafka Connect, Kafka Streams, KSQL, Kubernetes, Machine Learning, Microservices, Open Source on May 9th, 2018 by admin

I had a new talk presented at “Codemotion Amsterdam 2018” this week. I discussed the relation of Apache Kafka and Machine Learning to build a Machine Learning infrastructure for extreme scale.

Long version of the title:

Deep Learning at Extreme Scale (in the Cloud) 
with the Apache Kafka Open Source Ecosystem – How to Build a Machine Learning Infrastructure with Kafka, Connect, Streams, KSQL, etc.

As always, I want to share the slide deck. The talk was also recorded. I will share the video as soon as it was published by the organizer.

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Agile Cloud-to-Cloud Integration with iPaaS, API Management and Blockchain

Posted in API Management, Blockchain, Cloud, Cloud-Native, Docker, EAI, ESB, Microservices, Middleware on April 23rd, 2017 by Kai Wähner

Cloud-to-Cloud integration is part of a hybrid integration architecture. It enables to implement quick and agile integration scenarios without the burden of setting up complex VM- or container-based infrastructures. One key use case for cloud-to-cloud integration is innovation using a fail-fast methodology where you realize new ideas quickly. You typically think in days or weeks, not in months. If an idea fails, you throw it away and start another new idea. If the idea works well, you scale it out and bring it into production to a on premise, cloud or hybrid infrastructure. Finally, you make expose the idea and make it easily available to any interested service consumer in your enterprise, partners or public end users.

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Cloud Native Middleware Microservices – 10 Lessons Learned (O’Reilly Software Architecture 2017, New York)

Posted in API Management, Cloud, Cloud-Native, Docker, EAI, ESB, Microservices, Middleware, SOA on April 5th, 2017 by Kai Wähner

I want to share my slide deck and video recordings from the talk “10 Lessons Learned from Building Cloud Native Middleware Microservices” at O’Reilly Software Architecture April 2017 in New York, USA in April 2017.

Abstract
Microservices are the next step after SOA: Services implement a limited set of functions; services are developed, deployed, and scaled independently; continuous delivery automates deployments. This way you get shorter time to results and increased flexibility. Containers improve things even more, offering a very lightweight and flexible deployment option.

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

Posted in API Management, Cloud, Cloud-Native, Docker, EAI, ESB, Java / JEE, Microservices, Middleware, SOA on February 24th, 2017 by Kai Wähner

The following shows a case study about successfully moving from a very complex monolith system to a cloud-native architecture. The architecture leverages containers and Microservices. This solve issues such as high efforts for extending the system, and a very slow deployment process. The old system included a few huge Java applications and a complex integration middleware deployment.

The new architecture allows flexible development, deployment and operations of business and integration services. Besides, it is vendor-agnostic so that you can leverage on-premise hardware, different public cloud infrastructures, and cloud-native PaaS platforms.

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Comparison of Open Source IoT Integration Frameworks

Posted in API Management, Cloud, Cloud-Native, Microservices, SOA on November 3rd, 2016 by Kai Wähner

In November 2016, I attended Devoxx conference in Casablanca. Around 1500 developers participated. A great event with many awesome speakers and sessions. Hot topics this year besides Java: Open Source Frameworks, Microservices (of course!), Internet of Things (including IoT Integration), Blockchain, Serverless Architectures.

I had three talks:

  • How to Apply Machine Learning to Real Time Processing
  • Comparison of Open Source IoT Integration Frameworks
  • Tools in Action – Live Demo of Open Source Project Flogo

In addition, I was interviewed by the Voxxed team about Big Data, Machine Learning and Internet of Things. The video will be posted on Voxxed website in the next weeks.

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Open Source Project Flogo – Overview, Architecture and Live Demo

Posted in API Management, Cloud, Cloud-Native, Docker, ESB, Microservices, Middleware, SOA on November 3rd, 2016 by Kai Wähner

In October 2016, the open source IoT integration framework Flogo was published as first developer preview. This blog post is intended to give a first overview about Flogo. You can either browse through the slide deck or watch the videos.

Flogo

What is Project Flogo?

In short, Flogo is an ultra-lightweight integration framework powered by Go programming language. It is open source under the permissive BSD license and easily extendable for your own use cases. Flogo is used to develop IoT edge apps or cloud-native / serverless microservices. Therefore, it is complementary to other integration solutions and IoT cloud platforms.

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Trends at JavaOne 2016: Microservices, Docker, Cloud-Native Middleware

Posted in Cloud, Cloud-Native, Docker, ESB, Java / JEE, Microservices, Middleware, SOA on September 23rd, 2016 by Kai Wähner

Like every year, I attended JavaOne (part of Oracle World) in San Francisco in late September 2016. This is still one of the biggest conferences around the world for technical experts like developers and architects.

I planned to write a blog posts about new trends from the program, exhibition and chats with other attendees. Though, I can make it short: 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. It felt like 80% of non-Java talks were about these three topics. The other 20% were Internet of Things (IoT), DevOps and some other stuff. Middleware was also a hot topic. Not always directly, but I was in several talks focusing on integration, orchestration of microservices, (IoT) gateways.

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TIBCO’s Hybrid Integration Platform

Posted in API Management, Cloud, Cloud-Native, Docker, EAI, ESB, Microservices, SOA on September 14th, 2016 by Kai Wähner

[Originally posted on the TIBCO Blog]

The IT world is moving forward rapidly. The digital transformation changes complete industries and peels away existing business models. Cloud services, mobile devices, and the Internet of Things establish wild spaghetti architectures through different departments and lines of business. Several different concepts, technologies, and deployment options are used. A single integration backbone is not sufficient in this era anymore.

A hybrid integration platform for core and edge services

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