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.
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.
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.
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.
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.
Comparison: Data Preparation vs. Inline Data Wrangling in Machine Learning and Deep Learning ProjectsPosted 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.
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.
In October 2016, the open source IoT integration framework Flogo was published as first developer preview. This blog post is intended to give a first overview about Flogo. You can either browse through the slide deck or watch the videos.
What is Project Flogo?
In short, Flogo is an ultra-lightweight integration framework powered by Go programming language. It is open source under the permissive BSD license and easily extendable for your own use cases. Flogo is used to develop IoT edge apps or cloud-native / serverless microservices. Therefore, it is complementary to other integration solutions and IoT cloud platforms.
Comparison Of Log Analytics for Distributed Microservices – Open Source Frameworks, SaaS and Enterprise ProductsPosted 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:
[Originally posted on the TIBCO Blog]
The IT world is moving forward rapidly. The digital transformation changes complete industries and peels away existing business models. Cloud services, mobile devices, and the Internet of Things establish wild spaghetti architectures through different departments and lines of business. Several different concepts, technologies, and deployment options are used. A single integration backbone is not sufficient in this era anymore.
A hybrid integration platform for core and edge services
The IT world is moving forward fast. The digital transformation changes complete industries and peels away existing business models. Cloud services, mobile devices and the Internet of Things establish wild spaghetti architectures though different departments and lines of business. Several different concepts, technologies and deployment options are used. A single integration backbone is not sufficient anymore in this era of integration. Therefore, a Hybrid Integration Architecture is getting the new default in most enterprises.
Different user roles need to leverage different tools to integrate applications, services and APIs for their specific need. A key for success is that all integration and business services work together across different platforms in a hybrid world with on premise and cloud deployments.