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Companies across industries are pushing to move data and workloads to the cloud, whether as part of digital transformations or to avoid building costly new infrastructure to handle growing demand. For many organizations, key to this move are containers and Kubernetes — especially when multiple cloud services are involved. [ Stay on budget with these 6 cloud cost management tips, learn the 5 fundamentals of effective cloud management and beware hidden cloud migration gotchas. | Get the latest cloud computing insights by signing up for our newsletter. ] Also featured in this series: Containers march into the mainstream (InfoWorld) Kubernetes meets the real world (InfoWorld) Essential things to know about container networking (Network World) How Visa built its own container security solution (CSO) PaaS, CaaS, or FaaS? How to choose (InfoWorld) Containers on the desktop? You bet -- on Windows 10X (Computerworld) Containers are standalone software packages that

GOOD with numbers? Fascinated by data? The sound you hear is opportunity knocking. Mo Zhou was snapped up by I.B.M. last summer, as a freshly minted Yale M.B.A., to join the technology company’s fast-growing ranks of data consultants. They help businesses make sense of an explosion of data — Web traffic and social network comments, as well as software and sensors that monitor shipments, suppliers and customers — to guide decisions, trim costs and lift sales. “I’ve always had a love of numbers,” says Ms. Zhou, whose job as a data analyst suits her skills. To exploit the data flood, America will need many more like her. A report last year by the McKinsey Global Institute, the research arm of the consulting firm, projected that the United States needs 140,000 to 190,000 more workers with “deep analytical” expertise and 1.5 million more data-literate managers, whether retrained or hired. The impact of data abundance extends well beyond business. Justin Grimmer, for example,

earning java can give you headaches if you are a beginner. Why? Because before starting to learn Java programming, you need to prepare your machine. You need to install everything you need for java programming, making it suitable for coding in Java language.   But don’t you worry…we will arm you with all the tools you need to get started, including this renowned Ultimate Java Tutorial for Beginners. First off, some introductions. Java is a high-level, object-oriented programming language developed by Sun Microsystems. Learning Java has its incentives, as it’s a powerful language for developing desktop applications, web applications, and many smart devices run on Java. Java is also a platform-independent programming language, highly portable. If your computer runs on Windows, Linux or Mac OS, they all are same for Java because it runs on virtual machine. javastackChecklist before you start coding So, first things first – before writing your first code in Java, you need to

The word "Docker" refers to several things, including an open source community project; tools from the open source project; Docker Inc., the company that primarily supports that project; and the tools that company formally supports. The fact that the technologies and the company share the same name can be confusing. Here's a brief explainer: The IT software "Docker” is containerization technology that enables the creation and use of Linux® containers. The open source Docker community works to improve these technologies to benefit all users. The company, Docker Inc., builds on the work of the Docker community, makes it more secure, and shares those advancements back to the greater community. It then supports the improved and hardened technologies for enterprise customers. With Docker, you can treat containers like extremely lightweight, modular virtual machines. And you get flexibility with those containers—you can create, deploy, copy, and move them

The MEAN stack – also known as MEAN.js – is the collection of open source, JavaScript-based technologies that has changed the way developers build web and mobile applications. MEAN is an acronym, which stands for: MongoDB Express AngularJS Node.js The term was first coined by MongoDB developer Valeri Karpov in this 2013 blog post, where he outlines the advantages of using a single language throughout a technology stack. This single language programming differentiates MEAN over the then-popular LAMP stack. Short for Linux, Apache, MySQL and PHP, LAMP required developers to know various languages like C, C++, Perl, Python, PHP and SQL. When it comes to the MEAN stack, JavaScript is king, the most commonly used language among developers according to the 2018 StackOverflow Developer Survey, with JSON as its common dialect. Let’s take a look at each component below. MongoDB: The Database MongoDB - Part of the MEAN stack MongoDB is the NoSQL database

Defined as the speed at which an app loads and responds to a member, app performance is critical to an app’s success. When an app responds slowly to a member interaction, it’s an unsatisfactory experience. In order to maintain a reliable and consistent member experience, we have a dedicated performance engineering team to monitor and troubleshoot performance issues. However, the process for identifying negative trends and the underlying causes of poor performance has not always been as sophisticated and nimble as it is today. Our Android app runs on a cycle of performance operations: monitor, profile, optimize, and ramp. This cycle has helped us not only maintain a solid app performance, but introduce improvements over time. Since 2018, our Android engineers have reduced the app startup time by over 700ms—that’s 28% of a total 2.5s at the 90th percentile. In the first of this two-part blog series, we’ll introduce how we monitor and profile our Android app to find opportuniti

The Kubernetes kommunity The secret to Kubernetes’ popularity is no secret: community. As I wrote in 2016, Kubernetes wasn’t first to market (Mesosphere and Docker get that honor). Nor was it the only open source container orchestration tool on the market. What it was, however, was open. It’s possible to be open source but have closed governance, thwarting would-be contributors (and competitors). Google, however, took a different tactic, as I wrote then: What accounts for these wildly disparate community results [between Kubernetes, Docker, and Apache Mesos]? In a word: Google—or rather, the relative lack of Google. While each of the other orchestration projects comes with a heavy dose of single-vendor influence, Kubernetes benefits from Google’s hands-off approach to ongoing development, as well as its original engineering. Five years in, Google remains the single-biggest contributor to Kubernetes, followed by VMware and Red Hat (measuring by last year’s contributions). B

History and evolution of big data analytics The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term “big data,” businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends. The new benefits that big data analytics brings to the table, however, are speed and efficiency. Whereas a few years ago a business would have gathered information, run analytics and unearthed information that could be used for future decisions, today that business can identify insights for immediate decisions. The ability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before. The Importance of Big Data Analytics Graphic Why is big data analytics important? Big data

What are containers? One of the goals of modern software development is to keep applications on the same host or cluster isolated from one another so they don’t unduly interfere with each other’s operation or maintenance. This can be difficult, thanks to the packages, libraries, and other software components required for them to run. One solution to this problem has been virtual machines, which keep applications on the same hardware entirely separate, and reduce conflicts among software components and competition for hardware resources to a minimum. But virtual machines are bulky—each requires its own OS, so is typically gigabytes in size—and difficult to maintain and upgrade. Containers, by contrast, isolate applications’ execution environments from one another, but share the underlying OS kernel. They’re typically measured in megabytes, use far fewer resources than VMs, and start up almost immediately. They can be packed far more densely on the same hardware and spun up and