Microsoft Set to Release Office 2016, Built for Mobile, Social, Cloud

ANGUS LOTEN | April 26, 2016

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Microsoft Corp.’s Office 2016, set to be released this month, is poised to overhaul a key franchise for the mobile, cloud and social era, analysts say. More platform than product, the new suite has been adapted to work across desktops and devices, a move that began with the introduction of the cloud-based Office 365 subscription service years earlier. Much in the same way Internet Explorer 20 years ago incorporated the best features of rival Web browsers, the new Office leverages some of the major developments in social and collaborative work, with better multi-authoring and sharing functionalities.

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Palo Alto Networks Advances DevSecOps in the Cloud

Article | April 7, 2020

New capabilities include the ability to define and apply cybersecurity policies to continuous integration (CI) and continuous delivery (CD) workflows and scanning tools for discovering misconfigurations in cloud infrastructure templates, which is the most common source of cloud security issues.Palo Alto Networks has also added the ability to scan virtual machines native to the Amazon Web Services (AWS) before they are deployed as well as an ability to apply policies to workloads running on the AWS Lambda serverless computing framework via a single click. That capability eliminates the need to manually install wrappers in application code running on the AWS Lambda framework.

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Avoiding public cloud? Telcos are running out of excuses

Article | February 28, 2020

The need for telecoms operators to digitally transform isn’t just being stressed by outsiders. Telcos themselves are making a significant push toward becoming more nimble, digital businesses to improve subscriber experiences and drive higher revenues. This push toward digitalisation is, says Tim McElligott, senior analyst at TM Forum, driving telcos to consider significant infrastructure changes.

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What Is Cloud-Native and Why Does it Matter for CI

Article | February 11, 2020

Continuous intelligence (CI) relies on the real-time analysis of streaming data to produce actionable insights in milliseconds to seconds. Such capabilities have applications throughout a business. In today’s dynamic marketplace, new CI applications that use data from various sources at any given time might be needed on very short notice.The challenge is how to have the flexibility to rapidly develop and deploy new CI applications to meet fast-changing business requirements. A common approach employed today is to use a dynamic architecture that delivers access to data, processing power, and analytics capabilities on demand. In the future, solutions also will likely incorporate artificial intelligence applications to complement the benefits of traditional analytics. Increasingly, cloud-native is the architecture of choice to build and deploy AI-embedded CI applications. A cloud-native approach offers benefits to both the business and developers. Cloud-native applications or services are loosely coupled with explicitly described dependencies.

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How to Master Multi-Cloud Data Complexities

Article | May 20, 2020

The current patterns of cloud migration include simple “lift and shift,” which moves data with as little work as possible, typically by refactoring or redoing the applications and data so they work more efficiently on a cloud-based platform. More and more migrations include multi-cloud, which contributes to the appearance of new data complexity issues. When leveraging multi-cloud architectures, it’s important for IT leaders and cloud professionals to rethink how to deal with data complexity. If businesses are tasked with this massive and growing data management problem, it seems to me they ought to get their IT house in order. That means across a vast heterogeneity of systems, deployments, and data types. That should happen in order to master the data equation for your lines of business applications and services. Table of Contents What is multi-cloud? Why use multiple clouds? How to manage multi-cloud data complexities? What is multi-cloud? Multi-cloud is the use of two or more cloud computing services, including any combination of public, private, and hybrid. The end result is the capacity to orchestrate resources across multiple private or public cloud platforms that contain multiple cloud vendors, accounts, availability zones, or regions or premises. Why use multiple clouds? The three most important benefits of utilizing multiple clouds are: High availability – The multi-cloud provides protection for an organization’s data storage against threats. If a cloud is unavailable, other clouds remain online to run applications. Flexibility – Multi-cloud gives businesses the option to select the “best” of each cloud to suit their particular needs based on economics, location, and timing. Avoid Vendor Lock-In – This allows the application, workload, and data to be run in any cloud based on business or technical requirements at any given time. Cost effectiveness – Multi-cloud enables businesses to control their costs by optimizing the public cloud and choosing infrastructure vendors based on price. Public cloud services deliver functionality without having to hire personnel. Multi-cloud allows you to choose the right platform for your application and customers while using the best features from each cloud service provider. This gives companies the flexibility they need to select the “best” of each cloud to suit their particular needs based on economics, location, and timing. Multi-cloud also provides protection against the failure of a single cloud platform. Large enterprises may also be able to maximize the benefits of different infrastructure vendors that are competing on price for their business (smaller companies won’t have this luxury). Cloud is very different from your internal IT stuff — the way you program it, the way you develop applications. It has a wonderful cost proposition, at least initially. But now, of course, these companies have to deal with all of this complexity. - Martin Hingley, President and Market Analyst, IT Candor Limited Read more: How To Derive Data Insights In Hybrid Cloud Model And Drive Innovation How to manage multi-cloud data complexities? The reasons for the rising data complexity issues are fairly well known and include the following: The rising use of unstructured data that doesn’t have native schemas. Schemas are typically defined at access. The rising use of streaming data that many businesses employ to gather information as it happens and then process it in flight. The rise of IoT devices that spin off massive amounts of data. The changing nature of transactional databases, moving to NoSQL and other non-relational models. The continued practice of binding single-purpose databases to applications. Finally, and most importantly, the rise of as-a-service cloud-based and cloud-only databases, such as those now offered by all major cloud providers that are emerging as the preferred databases for applications built both inside and outside of the public clouds. Moreover, the use of heterogeneous distributed databases within multi-cloud architectures are preferred. Challenge of multi-cloud For the most part, those who build today’s data systems just try to keep up rather than get ahead of data complexity issues. The migration of data to net-new systems in multi-clouds is more about tossing money and database technology at the problem than solving it. Missing is core thinking about how data complexity should be managed, along with data governance and data security. We’re clearly missing the use of new approaches and helpful enabling technology within multi-cloud deployments that will remove the core drawbacks of data complexity. The challenge is that you need a single version of the truth. Lots of IT organizations don’t have that. Data governance is hugely important; it’s not nice to have, it’s essential to have. - Martin Hingley, President and Market Analyst, IT Candor Limited The core issue is to move toward application architectures that decouple the database from the applications, or even move toward collections of services, so you can deal with the data at another layer of abstraction. The use of abstraction is not new, but we haven’t had the required capabilities until the last few years. These capabilities include master data management (MDM), data service enablement, and the ability to deal with the physical databases using a configuration mechanism that can place volatility and complexity into a single domain. Virtual databases are a feature of database middleware services that technology suppliers provide. They serve to drive a configurable structure and management layer over existing physical databases, if that layer is in the requirements. This means that you can alter the way the databases are accessed. You can create common access mechanisms that are changeable within the middleware and do not require risky and expensive changes to the underlying physical database. Moving up the stack, we have data orchestration and data management. These layers provide enterprise data management with the ability to provide services such as MDM, recovery, access management, performance management, etc., as core services that exist on top of the physical or virtual databases, in the cloud or local. Moving up to the next layer, we have the externalization and management of core data services or microservices. These are managed, governed, and secured under common governance and security layers that can track, provision, control, and provide access to any number of requesting applications or users. ACT NOW Most enterprises are ignoring the rapid increase of data, as well as that of data complexity. Many hope that something magical will happen that will solve the problem for them, such as standards. The rapid rise in the use of multi-cloud means that your data complexity issues will be multiplied by the number of public cloud providers that end up being part of your multi-cloud. So, we’ll see complexity evolve from a core concern into a major hindrance to making multi-cloud deployment work effectively for the business. What’s needed now is to understand that a problem exists, and then think through potential solutions and approaches. Once you do that, the technology to employ is rather easy to figure out. Don’t make the mistake of tossing tools at the problem. Tools alone won’t be able to deal with the core issues of complexity. Considering the discussion above, you can accomplish this in two steps. First, define a logical data access layer that can leverage any type of back-end database storage system. Second, define metadata management with the system use of both security and governance. The solution occurs at the conceptual level, not with the introduction of another complex array of technology on top of already complex arrays of technology. It’s time to realize that we’re already in a hole. Stop digging. Read more:Flexible building blocks for the new cloud and data-driven world

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