KSQL Deep Dive – The Open Source Streaming SQL Engine for Apache Kafka

Posted in Apache Kafka, Big Data, Confluent, Docker, Java / JEE, Kafka Connect, Kafka Streams, KSQL, Microservices, Open Source, Stream Processing on May 15th, 2018 by Kai Wähner

I had a workshop at Kafka Meetup Tel Aviv in May 2018: “KSQL Deep Dive – The Open Source Streaming SQL Engine for Apache Kafka“.

Here are the agenda, slides and video recording.

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Rethinking Stream Processing with Apache Kafka, Kafka Streams and KSQL

Posted in Apache Kafka, Big Data, Docker, Java / JEE, Kafka Streams, KSQL, Kubernetes, Messaging, Microservices, Open Source, Stream Processing on March 13th, 2018 by admin

I presented at JavaLand 2018 in Brühl recently. A great developer conference with over 1800 attendees. The location is also awesome! A theme park: Phantasialand. My talk: “New Era of Stream Processing with Apache Kafka’s Streams API and KSQL“. Just want to share the slide deck…

Kai Speaking at JavaLand 2018 about Kafka Streams and KSQL

Abstract

Stream Processing is a concept used to act on real-time streaming data. This session shows and demos how teams in different industries leverage the innovative Streams API from Apache Kafka to build and deploy mission-critical streaming real time application and microservices.

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Video Recording – Apache Kafka as Event-Driven Open Source Streaming Platform (Voxxed Zurich 2018)

Posted in Apache Kafka, Big Data, Cloud, Docker, EAI, ESB, Integration, Java / JEE, Kafka Connect, Kafka Streams, KSQL, Kubernetes, Messaging, Microservices, Middleware, Open Source, SOA, Stream Processing on March 13th, 2018 by admin

I spoke at Voxxed Zurich 2018 about Apache Kafka as Event-Driven Open Source Streaming Platform. The talk includes an intro to Apache Kafka and its open source ecosystem (Kafka Streams, Connect, KSQL, Schema Registry, etc.). Just want to share the video recording of my talk.

Abstract

This session introduces Apache Kafka, an event-driven open source streaming platform. Apache Kafka goes far beyond scalable, high volume messaging. In addition, you can leverage Kafka Connect for integration and the Kafka Streams API for building lightweight stream processing microservices in autonomous teams. The open source Confluent Platform adds further components such as a KSQL, Schema Registry, REST Proxy, Clients for different programming languages and Connectors for different technologies and databases. Live Demos included.

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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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Deep Learning in Real Time with TensorFlow, H2O.ai and Kafka Streams (Slides from JavaOne 2017)

Posted in Analytics, Apache Kafka, Big Data, Business Intelligence, Confluent, Deep Learning, Docker, Java / JEE, Kafka Streams, Machine Learning, Microservices, Open Source, Stream Processing on October 4th, 2017 by Kai Wähner

Early October… Like every year in October, it is time for JavaOne and Oracle Open World in San Francisco… I am glad to be back at this huge event again. My talk at JavaOne 2017 was all about deployment of analytic models to scalable production systems leveraging Apache Kafka and Kafka Streams. Let’s first look at the abstract. After that I attach the slides and refer to further material around this topic.

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Why I Move (Back) to Open Source for Messaging, Integration and Stream Processing

Posted in Analytics, API Management, Big Data, Blockchain, Cloud, Cloud-Native, Docker, ESB, Hadoop, Internet of Things, Java / JEE, Machine Learning, Microservices, Middleware, SOA on May 1st, 2017 by Kai Wähner

After three great years at TIBCO Software, I move back to open source and join Confluent, a company focusing on the open source project Apache Kafka to build mission-critical, scalable infrastructures for messaging, integration and streaming analytics. Confluent is a Silicon Valley startup, still in the beginning of its journey, with a 700% growing business in 2016, and is exjustpected to grow significantly in 2017 again.

In this blog post, I want to share why I see the future for middleware and big data analytics in open source technologies, why I really like Confluent, what I will focus on in the next months, and why I am so excited about this next step in my career.

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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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