In today’s competitive business environment, it is more important than ever to develop applications not only accurately but quickly. The traditional “waterfall” method is effective, but requires so many steps that the process… Continue reading
Organizations that prioritize a multi-cloud strategy want the ability to lift and shift cloud applications from one cloud to another, or even run that app on multiple clouds at the same time. But… Continue reading
All software developers are trained in secure coding. Making sure deployed software is safe from various nefarious threats is always a top priority. Or, at least, it should be a top priority. Many… Continue reading
Today’s businesses are faced with a singular reality: innovation is a requirement for mere survival. Yet many enterprises are crippled by legacy and technical debt. – … Continue reading
To properly prepare cloud-based applications for containers, be sure to emphasize a service-based architecture and understand your management options upfront.
Container technology is an increasingly popular choice for application hosting in the enterprise. But,… Continue reading
Machine learning isn’t only in the cloud. Microsoft is bringing it to PCs in the next Windows 10 release. Here’s how to get started now.
We’re not far away from a new release of Windows 10, and with it plenty of new APIs for your applications. One big change is support for running trained machine learning models as part of Windows applications, taking advantage of local GPUs to accelerate machine learning applications.
Building a machine learning application can be a complex process. Training a model can require a lot of data, and a considerable amount of processing power. That’s fine if you’ve got access to a cloud platform and lots of bandwidth, but what if you want to take an existing model from GitHub and run it on a PC?
Trained machine learning models are an ideal tool for bringing the benefits of neural networks and deep learning to your applications. All you should need to do is hook up the appropriate interfaces, and they should run as part of your code. But with many machine learning frameworks and platforms, there’s a need for a common runtime that can use any of the models out there. That’s where the new Windows machine learning tools come into play, offering Windows developers a platform to run existing machine learning models in their applications, taking advantage of a developing open standard for exchanging machine learning models. Continue reading
A multi-cloud strategy reduces vendor lock-in and outage risks. But to realize those benefits, development teams must first design apps to successfully run on various platforms.
In software development today, the cloud is a fact of life. And, increasingly, enterprises plan their application architectures across multiple public cloud providers, rather than just one.
A key driver behind multi-cloud adoption is increased reliability. In 2017, Amazon’s Simple Storage Service went down due to a typo in a command executed during routine maintenance. In the pre-cloud era, the consequences of an error like that would be relatively negligible. But, due to the growing dependence on public cloud infrastructure, that one typo reportedly cost upwards of $150 million in losses across many companies.
A multi-cloud app — or an app designed to run on various cloud-based infrastructures — helps mitigate these risks; if one platform goes down, other steps in to take its place. Continue reading
Demands for digital transformation in business may ring hollow to some architects. However, componentization can play a key role in making business innovation a reality. – Software engineering
Architects and developers have too many priorities and too many high-level goals as it is. The introduction of broad business imperatives, such as application modernization and digital transformation, do little to create a technical approach or define a software architecture.
The best way to really approach these demands is to get your terms straight, frame applications along modern cloud component lines, introduce component-based software engineering to make processes match business needs and recognize productivity and technology trends as equals.
A primary technical step in creating a business transformation bridge is to focus on component-based software engineering, part of which involves having a componentization strategy. Componentization is driven by two forces: functional requirements from the business transformation side and technical requirements from the application modernization side.
The functional side of componentization is created by the tasks workers perform and the application tools they use. The goal of functional componentization is to create components at the highest level and avoid a specialization of functions that limit sharing components among applications. Continue reading
Multi-cloud, blockchain and more sophisticated PaaS tools are three trends expected to reshape app development practices in 2018. Is your team ready for the change? – App development
Experts expect 2018 to be a year of change for cloud app development, as trends like DevOps, hybrid cloud and blockchain continue to take off.
Here’s a closer look at these and other emerging trends, what they’ll mean for enterprise app development teams and the potential challenges and risks they pose.
In 2018, vendors will attempt to improve their platform as a service (PaaS) offerings to speed up application development and support DevOps workflows. Leading cloud providers, such as Amazon Web Services (AWS), Google, Microsoft, IBM, and Oracle, will continue to roll out tooling to automatically provision PaaS-like capabilities via containers and Kubernetes. In addition, CIOs and app development teams will start to take advantage of higher-level service mesh tools, such as Istio and Linkerd, to recreate the benefits of monolithic apps within a collection of microservices.
However, these more sophisticated cloud tools come with potential vendor lock-in risks, said David Bartoletti, VP and principal analyst at Forrester. For example, AWS provides a set of tools to automatically provision containers with its Elastic Container Service, but those tools won’t necessarily help developers who need to move apps to private infrastructure or manage apps that span cloud platforms. Continue reading
Office-based employees have a wealth of software tools available to keep them connected with colleagues and the wider business. Even those working remotely can easily stay in touch with their team through email, enterprise social networks, and group messaging tools such as Slack.
That’s not always the case for deskless workers – the vast, yet the underserved chunk of the workforce that tends to fall outside the scope of IT, according to Stacey Epstein, CEO of enterprise messaging app vendor Zinc.
Zinc specifically targets employees in non-office-based roles. They could be anything from emergency workers to construction laborers, nurses, retail workers or service technicians – employees who usually own a smartphone but don’t routinely require access to core business applications.
“Deskless workers are doing their job by fixing something in the field or helping a customer at a hotel desk or a retail store, or if they are in healthcare they are seeing a patient,” said Epstein. “They are not sitting in front of a computer or an office where they can hop into a conference room or even have an impromptu team or group meeting.
“So these workers are inherently siloed from the people and the knowledge that help them do a good job,” she said.
Deskless workers have different technology requirements than typical office workers, said 451 Research senior analyst Raul Castañón-Martínez. They don’t use a computer as their main device to do their job and communications are often sent via text or consumer apps such as WhatsApp. Continue reading