> Issue XCV, Q2/2016
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Containers and Containerization – Applications and Services on Steroids?

Roger Stoffers

Roger Stoffers

Containerization is a term originating from the transportation industry. A container is an independent self-contained (autonomous) unit of freight with fixed dimensions, also known as a fixed interface. Such a container is used in international transportation where all transportation devices (trucks, ships, trains, cranes) handle containers (containing disparate payload) in a unified way, meaning the payload can be moved back and forth in an automated way. A similar approach has made its way to the IT world in recent years, with the advent of containerization as a promising technology to succeed or complement virtualization. Before discussing the implications of IT container technology, let's see how it is similar, yet fundamentally different from virtualization. Both containers as well as virtual machines (virtualization) are means to increase the effectiveness and portability of hosting workloads on servers. Both allow workload to be abstracted from the underlying physical hardware but each takes a different...

Containerized Service Deployment

Roger Stoffers

Roger Stoffers

The SOA Design Patterns Catalog has been extended with the newly authored Containerized Service Deployment Pattern. This pattern formalizes the Containerized Service Deployment model and specifies its distinct characteristics.

Big Data Adoption and Planning Considerations

Paul Buhler, Thomas Erl, Wajid Khattak

Paul Buhler

Thomas Erl

Wajid Khattak

Big Data initiatives are strategic in nature and should be business-driven. The adoption of Big Data can be transformative but is more often innovative. Transformation activities are typically low-risk endeavors designed to deliver increased efficiency and effectiveness. Innovation requires a shift in mindset because it will fundamentally alter the structure of a business either in its products, services or organization. This is the power of Big Data adoption; it can enable this sort of change. Innovation management requires careÑtoo many controlling forces can stifl e the initiative and dampen the results, and too little oversight can turn a best intentioned project into a science experiment that never delivers promised results. It is against this backdrop that Chapter 3 addresses Big Data adoption and planning considerations. Given the nature of Big Data and its analytic power, there are many issues that need to be considered and planned for in the...

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