Red Hat Summit 2017 – Planning your JBoss labs

This year in Boston, MA you can attend the Red Hat Summit 2017, the event to get your updates on open source technologies and meet with all the experts you follow throughout the year.

It’s taking place from May 2-4 and is full of interesting sessions, keynotes, and labs.

This year I was part of the process of selecting the labs you are going to experience at Red Hat Summit and wanted to share here some to help you plan your JBoss labs experience. These labs are for you to spend time with the experts who will teach you hands-on how to get the most out of your JBoss middleware products.

Each lab is a 2-hour session, so planning is essential to getting the most out of your days at Red Hat Summit.

As you might be struggling to find and plan your sessions together with some lab time, here is an overview of the labs you can find in the session catalog for exact room and times. Each entry includes the lab number, title, abstract, instructors and is linked to the session catalog entry:

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Five Links: The More You Know Edition

Happy Friday, everyone.

There have been a couple of events lately that, at least tangentially, made me think about information and what we do with it. There have been a series of DDOS attacks on popular sites, at least one of which was driven by a blind army of smart devices. The other is the volatile and ultimately inaccurate polling leading into the US Presidential election. Both of these hint at the Wild West nature of technology — its flexibility and newness offers a lot of promise and a lot of unknown risks. So the theme for this week is — what is the quality of data and analytics and how do we do it “right.”

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New styles of integration are the hallmark of Digital Transformation

New Styles of Integration 2

Shakeup your integration strategy to enable digital transformation, says VP & Gartner Fellow Massimo Pezzini. Pezzini asserts that it is not just about transforming and modernizing the infrastructure and the applications concerned.  Some of the fundamental concepts of integration need to be revisited and transformed as well.  Such systemic transformation punctuate the migration of  legacy environments to microservices and the cloud.  What may have worked in the past will no longer be applicable going forward.  “Integration is dead.  Long live integration,” screamed the title of one of the sessions at the Red Hat Summit 2016.  The session was making a point.  Integration, as we knew it a few years back, is dead.  Integration in the digital world has a long life in the decades ahead.  Join me as I walk through the new styles of integration that are the hallmark of digital transformation.

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Summit Preview: Integration Highlights

It is almost time for Red Hat Summit, in lovely San Francisco. Are you ready? You still have time to register!

I am doing a handful of preview posts. The session list is amazing, with about a dozen different tracks or focus areas, everything from application development (the heart of middleware) to cloud to systems management to storage. There are so many gems in these sessions; if you can attend, try to hit these. And if not — keep an eye out on the Summit page and social channels like Vimeo and YouTube. A lot of presentations post recordings or slide decks after Summit, and there is an incredible variety of information.

Today, I want to look at integration paths. There are a lot of really diverse and complex topics here, being broken down into real-life examples, things like data virtualization, API management, integrating data streams from multiple sources and protocols.

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How To Use Data Virtualization to Boost Business Results

Organizations across a range of industries are sitting on treasure troves of data. For the business intelligence team with the right tools, they can mine this data and unearth brilliant insights that could lead to the creation of new products and services and improve customer service and retention rates. With the possibility of such rich opportunities mere queries away, it’s no wonder that IT departments are increasingly concerned about data usage for analytics.

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