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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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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Apache Kafka + Kafka Streams + Mesos = Highly Scalable Microservices

Posted in Apache Kafka, Apache Mesos, Big Data, Confluent, Docker, Java / JEE, Kafka Streams, KSQL, Microservices, Middleware, Open Source, Stream Processing on January 12th, 2018 by Kai Wähner

My latest article about Apache Kafka, Kafka Streams and Apache Mesos was published on Confluent’s blog:

Apache Mesos, Apache Kafka and Kafka Streams for Highly Scalable Microservices

This blog post discusses how to build a highly scalable, mission-critical microservice infrastructure with Apache Kafka, Kafka Streams, and Apache Mesos respectively in their vendor-supported platforms from Confluent and Mesosphere.

Apache Mesos, Apache Kafka and Kafka Streams for Highly Scalable Microservices

Have fun reading it and let me know if you have any feedback…

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Apache Kafka + Kafka Streams + Mesos / DCOS = Scalable Microservices

Posted in Apache Kafka, Apache Mesos, Big Data, Confluent, Docker, Hadoop, Java / JEE, Kafka Connect, Kafka Streams, KSQL, Kubernetes, Microservices, Open Source on October 27th, 2017 by Kai Wähner

I had a talk at MesosCon 2017 Europe in Prague about building highly scalable, mission-critical microservices with Apache Kafka, Kafka Streams and Apache Mesos / DCOS. I would like to share the slides and a video recording of the live demo.

Abstract

Microservices establish many benefits like agile, flexible development and deployment of business logic. However, a Microservice architecture also creates many new challenges. This includes increased communication between distributed instances, the need for orchestration, new fail-over requirements, and resiliency design patterns.

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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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Machine Learning Applied to Microservices

Posted in Analytics, Big Data, Business Intelligence, Cloud, Docker, Hadoop, Microservices, Middleware on October 20th, 2016 by Kai Wähner

I had two sessions at O’Reilly Software Architecture Conference in London in October 2016. It is the first #OReillySACon in London. A very good organized conference with plenty of great speakers and sessions. I can really recommend this conference and its siblings in other cities such as San Francisco or New York if you want to learn about good software architectures and new concepts, best practices and technologies. Some of the hot topics this year besides microservices are DevOps, serverless architectures and big data analytics respectively machine learning.

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Comparison Of Log Analytics for Distributed Microservices – Open Source Frameworks, SaaS and Enterprise Products

Posted in Analytics, Big Data, Business Intelligence, Cloud, Hadoop, Microservices, SOA on October 20th, 2016 by Kai Wähner

I had two sessions at O’Reilly Software Architecture Conference in London in October 2016. It is the first #OReillySACon in London. A very good organized conference with plenty of great speakers and sessions. I can really recommend this conference and its siblings in other cities such as San Francisco or New York if you want to learn about good software architectures and new concepts, best practices and technologies. Some of the hot topics this year besides microservices are DevOps, serverless architectures and big data analytics.

I want to share the slide of my session about comparing open source frameworks, SaaS and Enterprise products regarding log analytics for distributed microservices:

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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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