Apache Kafka + Machine Learning => Confluent Blog Post and Github Project

Posted in Apache Kafka, Big Data, Confluent, Deep Learning, Kafka Streams, KSQL, Machine Learning, Open Source, Stream Processing on October 27th, 2017 by Kai Wähner

I am happy that my first official Confluent blog post was published and want to link to it from by blog:

How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka

The post explains in detail how you can leverage Apache Kafka and its Streams API to deploy analytic models to a lightweight, but scalable, mission-critical streaming appilcation.

Apache Kafka Ecosystem for Machine Learning

Github Examples for Apache Kafka + Machine Learning

If you want to take a look directly at the source code, go to my Github project about Kafka + Machine Learning. It contains several examples how to combine Kafka Streams with frameworks like TensorFlow, H2O or DeepLearning4J.

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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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Kafka Streams + H2O.ai + TensorFlow (Video Recording / Live Demo)

Posted in Analytics, Apache Kafka, Big Data, Kafka Streams, Machine Learning, Open Source, Stream Processing on September 7th, 2017 by Kai Wähner

I do a lot of presentations these days at meetups and conferences with one focus: How to leverage Apache Kafka and Kafka Streams to apply analytic models (built with H2O, TensorFlow, DeepLearning4J and other frameworks) to scalable, mission-critical environments. As many attendees have asked me, I created a video recording about this talk (focusing on live demos).

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Apache Kafka Streams + Machine Learning (Spark, TensorFlow, H2O.ai)

Posted in Analytics, Apache Kafka, Apache Spark, Big Data, Confluent, Hadoop, Integration, Kafka Connect, Kafka Streams, Machine Learning, Messaging, Microservices, Open Source, Stream Processing on May 23rd, 2017 by Kai Wähner

I started at Confluent in May 2017 to work as Technology Evangelist focusing on topics around the open source framework Apache Kafka. I think Machine Learning is one of the hottest buzzwords these days as it can add huge business value in any industry. Therefore, you will see various other posts from me around Apache Kafka (messaging), Kafka Connect (integration), Kafka Streams (stream processing), Confluent’s additional open source add-ons on top of Kafka (Schema Registry, Replicator, Auto Balancer, etc.). I will explain how to leverage all this for machine learning and other big data technologies in real world production scenarios.

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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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Visual Analytics + Open Source Deep Learning Frameworks

Posted in Analytics, Big Data, Cloud, Hadoop, Machine Learning on April 24th, 2017 by Kai Wähner

Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist.

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

Posted in Analytics, Big Data, Business Intelligence, Hadoop on February 13th, 2017 by Kai Wähner

I want to highlight a new presentation about Data Preparation in Data Science projects:

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

Data Preparation as Key for Success in Data Science Projects

A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project.

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

Posted in Analytics, Big Data, Business Intelligence, Cloud, Hadoop on November 15th, 2016 by Kai Wähner

In November 2016, I am at Big Data Spain in Madrid for the first time. A great conference with many awesome speakers and sessions about very hot topics such as Apache Hadoop, Spark Spark, Streaming Processing / Streaming Analytics and Machine Learning. If you are interested in big data, then this conference is for you! My two talks:

  • How to Apply Machine Learning to Real Time Processing” (see slides and video recording from a similar conference talk).
  • Comparison of Streaming Analytics Options” (the reason for this blog post; an updated version of my talk from JavaOne 2015)
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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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