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Providing the needed data for application development and testing is a huge headache for most organizations. The problems are often the same across companies – speed, quality, cost, and control. Provisioning data can take days or weeks, every time a refresh is required. Using dummy data leads to quality problems. Creating physical copies of large data sets and sending them to distributed teams of developers eats up expensive storage and bandwidth resources. And, all of these copies proliferating the organization can lead to inconsistent masking and exposure of sensitive data.
Let's just nip the conflation of these terms in the bud, shall we?
"MIcro" is big these days. Both microservices and microsegmentation are having and will continue to have an impact on data center architecture, but not necessarily for the same reasons. There's a growing trend in which folks - particularly those with a network background - conflate the two and use them to mean the same thing.
They are not.
One is about the application. The other, the network. There is a relationship, but it's a voluntary one. They are two very different things and we need to ...
I read an insightful article this morning from Bernard Golden on DZone discussing the DevOps conundrum facing many enterprises today – is it better to build your own DevOps tools or go commercial? For Golden, the question arose from his observations at a number of DevOps Days events he has attended, where typically the audience is composed of startup professionals:
“I have to say, though, that a typical feature of most presentations is a recitation of the various open source products and components and how they integrated them to implement their solution. In a word, how they created their ho...
Mobile, social, Big Data, and cloud have fundamentally changed the way we live. “Anytime, anywhere” access to data and information is no longer a luxury; it’s a requirement, in both our personal and professional lives. For IT organizations, this means pressure has never been greater to deliver meaningful services to the business and customers.
New tools, technologies, and frameworks have evolved recently to help manage rapid shifts in the technology landscape, but they’re only as good as the underlying processes that exist within the organization. As lines of business change or add requiremen...
This is the final installment of the six-part series Microservices and PaaS.
It seems like forever since I attended Adrian Cockroft's meetup focusing on microservices. It's actually only been a couple of months, but much has happened since then: countless articles, meetups, and conference sessions focusing on microservices have been delivered, many meetings and design efforts at companies moving towards a microservices-based approach have been endured, and five installments of this blog series have been written.
There's no doubt that microservices, like containerization and DevOps, is a tren...
In “Thinking Like a Data Scientist – Part I”, we examined the challenges for getting the business users to think like data scientists when contemplating where and how to leverage big data to drive business value. We introduced a “Thinking Like a Data Scientist” process that starts with identifying and understanding the organization’s top-level strategic business initiatives, then uses a “Strategic Nouns” technique to create potential business questions that were descriptive, predictive or in nature.
Virtualization is everywhere. Enormous and highly profitable companies have been built on nothing but virtualization. And nowhere has virtualization made more of an impact than in Cloud Computing, the rampant and unprecedented adoption of which has been the direct result of the wide availability of virtualization software and techniques that enabled it. But does the cloud actually require virtualization?
In the first four parts of this series I presented an introduction to microservices along with a handful of emerging microservices patterns, and a discussion of some of the downsides and challenges to using microservices. The most recent installment of this series looked at ten ways that PaaS facilitates microservices development and adoption.
In this post I’ll cover some words of wisdom, advice intended for individuals, teams, and organizations considering a move to microservices. I've gleaned this advice from the various articles and meetups mentioned above, from dozens of discussions with ...
It was recently revealed that Linux-powered drones were released which has unveiled a new frontier for open source software. Drone control, once limited to proprietary software, has now entered the open source realm. This opens up new possibilities of more flexible platforms for many Internet-connected objects as well as sparking conversation among the open source community about its potential relationship with the Internet of Things (IoT).
Open source software has allowed drone builders and programmers to create a platform using a free open source software development kit. In some cases, i...
There’s a story that needs to be written, in fact it’s not a story it’s a piece of technical analysis. We need to know why Big Data connectivity is not as simple as connecting Lego building blocks.
Vendors in the ‘cloud ERP financials’ space are fond of using terms like multi-channel to talk about the need to bring together previously disconnected elements of data (including Big Data) for the new way we are supposed to work with technology..
One reason a lot of entrepreneurs flock to cloud-based services for their business is the cost. Native applications require licenses that are either too expensive or require elaborate set-up across all devices.
In a recent article on the Entrepreneur, Bask Iyer, the CIO and Senior Vice President of Technology at Juniper Networks responded to the question, 'what entrepreneurs should put in the cloud?' His answer: "Everything." Bask elaborates by pointing out the ability of cloud to scale-up and scale-down at ease, the ease with which you can deploy plug-and-play cloud solutions for almost ever...
The Big Data explosion in recent years has created a vast number of new technologies in the area of data processing, storage, and management. One of the biggest names to appear on the scene is Hadoop. In case you need a quick review, Hadoop is a Big Data storage system that takes in large amounts of data from servers and breaks it into smaller, manageable chunks. The technology is complex but at a high level the Hadoop ecosystem essentially takes a “divide and conquer” approach to processing Big Data instead of processing data in tables, as in a relational database like Oracle or MySQL.
I’ve been thinking a bit about microservices (μServices) recently. My immediate reaction is to think: “Isn’t this just yet another new term for the same stuff, Web Services->SOA->APIs->Microservices?” Followed shortly by the thought, “well yes it is, but there are some important differences/distinguishing factors.”
Microservices is an evolutionary paradigm born out of the need for simplicity (i.e., get away from the ESB) and alignment with agile (think DevOps) and scalable (think Containerization) development and deployment architectures. Martin Fowler and James Lewis defined nine core charac...
There’s a lot of discussion around managing outages in production via the likes of DevOps principles and the corresponding software development lifecycles that does enable higher quality output from development, however, one cannot lay all blame for “bugs” and failures at the feet of those responsible for coding and development. As developers incorporate features and benefits of these paradigm shift, there is a learning curve and a point of not-knowing-what-is-not-known. Sometimes, the only way to learn is to actually put code into production and monitor its performance and actions.
Right off the bat, Newman advises that we should "think of microservices as a specific approach for SOA in the same way that XP or Scrum are specific approaches for Agile Software development". These analogies are very interesting because my expectation was that microservices is a pattern. So I might infer that microservices is a set of process techniques as opposed to an architectural approach. Yet in the book, Newman clearly includes some elements of concept model and architecture as well as process and organization.
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Cloud computing software is eating the world, and each day is bringing new developments in this world.
Yesterday's debate about public vs. private has transformed into the reality of hybrid cloud: a recent survey shows that 74% of enterprises have a hybrid cloud strategy.
Meanwhile, 94% of enterprises are using some form of XaaS – software, platform, and Infrastructure as a Service.
Big Data | Analytics and the emerging Internet of Things (IoT) are driving exponentially increased demands on datacenters and developers alike, as we cross the zettabyte horizon this year.
Containers and microservices are now part of every PaaS conversation, and IaaS providers are increasingly competing for platform customers.
WebRTC continues to reform web communications, and DevOps is pushing its way into an enterprise IT world that is increasingly agile, lean, and continuous.
Through all this, Cloud Expo remains the single independent event where delegates and technology vendors can meet to experience and discuss the entire world of the cloud.
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