Apache Kafka, KSQL and Apache PLC4X for IIoT Data Integration and Processing

Posted in Analytics, Apache Kafka, Big Data, Cloud, Confluent, EAI, ESB, IIoT, Internet of Things, Java / JEE, Kafka Connect, Kafka Streams, KSQL, MQTT, Open Source, PLC4X, Stream Processing on September 2nd, 2019 by Kai Wähner

Data integration and processing is a huge challenge in Industrial IoT (IIoT, aka Industry 4.0 or Automation Industry) due to monolithic systems and proprietary protocols. Apache Kafka, its ecosystem (Kafka Connect, KSQL) and Apache PLC4X are a great open source choice to implement this IIoT integration end to end in a scalable, reliable and flexible way.

This blog post covers a high level overview about the challenges and a good, flexible architecture to solve the problems. At the end, I share a video recording and the corresponding slide deck. These provide many more details and insights.

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Kafka Operator for Kubernetes – Confluent Operator to establish a Cloud-Native Apache Kafka Platform

Posted in Apache Kafka, Apache Mesos, Cloud, Cloud-Native, Confluent, Docker, Kafka Connect, Kafka Streams, KSQL, Kubernetes, Microservices on July 29th, 2019 by Kai Wähner

Confluent Operator is now GA for production deployments (Download Confluent Operator for Kafka here). This is a Kafka Operator for Kubernetes which provides automated provisioning and operations of an Apache Kafka cluster and its whole ecosystem (Kafka Connect, Schema Registry, KSQL, etc.) on any Kubernetes infrastructure.

Confluent Operator Kafka Operator for Kubernetes Download

I want to share a slide deck which explains:

  • Why Kubernetes is getting more and more traction to build a cloud-native infrastructure
  • Why this is relevant for Apache Kafka and Confluent Platform
  • The challenges running Kafka on Kubernetes
  • How Confluent Operator solves these problems providing a powerful Kafka Operator for Kubernetes
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IoT Integration with Kafka Connect, REST / HTTP, MQTT, OPC-UA – Lightboard Video

Posted in Apache Kafka, Big Data, Cloud, Confluent, EAI, ESB, IIoT, Integration, Internet of Things, Kafka Connect, Kafka Streams, KSQL, Messaging, Middleware, MQTT, Open Source, PLC4X, Stream Processing on July 26th, 2019 by admin

I just want to share my lightboard video recording. I talk about IoT integration and processing with Apache Kafka using Kafka Connect, Kafka Streams, KSQL, REST / HTTP,  MQTT and OPC-UA. Use cases, alternative architectures and different integration options are discussed on whiteboard.

End-to-End IoT Integration from Edge to Confluent Cloud

In this lightboard, Confluent’s Kai Waehner (Technology Evangelist) and Konstantin Karantasis (Software Engineer) discuss use cases leveraging the Apache Kafka open source ecosystem as a streaming platform to process IoT data. The session shows architectural alternatives of how devices like cars, machines or mobile devices connect to Apache Kafka via IoT standards like MQTT or OPC-UA.

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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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