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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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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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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Streaming Analytics with Analytic Models (R, Spark MLlib, H20, PMML)

Posted in Analytics, Big Data, Business Intelligence, Hadoop, In Memory, NoSQL on March 3rd, 2016 by Kai Wähner

In March 2016, I had a talk at Voxxed Zurich about “How to Apply Machine Learning and Big Data Analytics to Real Time Processing”.

Kai_Waehner_at_Voxxed_Zurich

Finding Insights with R, H20, Apache Spark MLlib, PMML and TIBCO Spotfire

Big Data” is currently a big hype. Large amounts of historical data are stored in Hadoop or other platforms. Business Intelligence tools and statistical computing are used to draw new knowledge and to find patterns from this data, for example for promotions, cross-selling or fraud detection. The key challenge is how these findings can be integrated from historical data into new transactions in real time to make customers happy, increase revenue or prevent fraud.

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Intelligent BPM Suite (iBPMS): Implementation of a CRM Use Case

Posted in Analytics, Big Data, BPM, Business Intelligence, Cloud, ESB, In Memory, Social Network on December 3rd, 2014 by admin

Today, humans have to interpret large sets of different data to make a decision. Using gut feeling is nothing but gambling. Therefore, big data analytics is getting more and more important every year to make better decisions. However, just doing big data analytics is not enough. In many use cases, systematic and monitored human interactions are as important to get best outcomes.

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A New Front for SOA: Open API and API Management as Game Changer

Posted in Analytics, API Management, Cloud, ESB, SOA on October 22nd, 2014 by admin

In October 2014, I had a talk at Jazoon in Zurich, Switzerland: “A New Front for SOA: Open API and API Management as Game Changer

Business Perspective – Open API and API Management

Open API represent the leading edge of a new business model, providing innovative ways for companies to expand brand value and routes to market, and create new value chains for intellectual property. In the past, SOA strategies mostly targeted internal users. Open APIs target mostly external partners.

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Fundamentals of Stream Processing (IBM InfoSphere Streams, TIBCO StreamBase, Apache Storm) – Book Review

Posted in Analytics, Big Data, Hadoop on July 1st, 2014 by Kai Wähner

Internet of things, cloud and mobile are the major drivers for stream processing. Use cases are network monitoring, intelligent surveillance, but also less technical things such as inventory management or fraud detection. The book helps a lot to get a basic understanding about history, concepts and patterns of the stream processing paradigm.

“Fundamentals of Stream Processing: Application Design, Systems, and Analytics” (www.amazon.com/Fundamentals-Stream-Processing-Application-Analytics/dp/1107015545) is one of only few books available about stream processing. Published in 2014 by Cambridge University Press. Authors are Henrique C. M. Andrade (JP Morgan, New York), Bugra Gedik (Bilkent University, Turkey), Deepak S. Turaga (IBM Thomas J. Watson Research Center, New York).

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“Hadoop and Data Warehouse (DWH) – Friends, Enemies or Profiteers? What about Real Time?” – Slides (including TIBCO Examples) from JAX 2014 Online

Posted in Analytics, Big Data, Business Intelligence, Cloud, ESB, Hadoop on May 13th, 2014 by Kai Wähner

Slides from my talk “Hadoop and Data Warehouse (DWH) – Friends, Enemies or Profiteers? What about Real Time?” at JAX 2014 (Twitter #jaxcon) in Mainz are online. JAX is a great conference with interesting topics and many good speakers!

Content (Data Warehouse, Business Intelligence, Hadoop, Stream Processing)

Big data represents a significant paradigm shift in enterprise technology. Big data radically changes the nature of the data management profession as it introduces new concerns about the volume, velocity and variety of corporate data. New business models based on predictive analytics, such as recommendation systems or fraud detection, are relevant more than ever before. Apache Hadoop seems to become the de facto standard for implementing big data solutions. For that reason, solutions from many different vendors emerged on top of Hadoop.

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