We’re living in the golden age of software development

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Software, as Marc Andreessen says, is eating the world. Everything we do is mediated through someone’s code — running on a smart thermostat, a smartphone, the old familiar PC, or as a microservice in an ever-expanding cloud.

We’re finally delivering on the decades-old promise of a ubiquitous computing world. But more than that, from the developer’s standpoint, the tools available to us are better and more sophisticated than ever. We’re also seeing monolithic applications break apart into services and platforms, ready to become part of your applications.

At the heart of the current wave of change are new design patterns suited to a highly distributed, asynchronous computing world. Yes, we’re still building n-tier MVC and MVVM apps — and we’ll carry on building them for a long time to come. But new microservice-focused design patterns give us a new set of tools to help us build highly scalable, concurrent applications that can handle the eventual consistency that comes from working with asynchronous services.

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Oracle hops on the bandwagon to dump Java browser plug-in

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With browser plug-ins going the way of the dinosaur, Oracle plans to deprecate its Java browser plug-in in Java Development Kit 9, which is due in March 2017. The technology will then be completely removed from Oracle JDK and the Java Runtime Environment (JRE) in a future release of Java Standard Edition.

Oracle is encouraging developers to migrate Java Applets, which depend on a plug-in, to the plug-in-free Java Web Start technology. In a blog post this week, Oracle’s Dalibor Topic noted this move away from plug-ins. “By late 2015, many browser vendors have either removed or announced timelines for the removal of standards-based plug-in support, eliminating the ability to embed Flash, Silverlight, Java, and other plug-in based technologies,” he said.

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Zing! Azul JVM raises the roof for in-memory Java applications

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Azul Systems, which has focused on building Java runtimes and JVM technologies for large enterprises, is looking to boost performance with expanded in-memory capacity for heavy-duty Java users.

The company is doubling the in-memory capacity of its Zing JVM to 2TB with Zing version 16.01. With 2TB, applications can use all the memory in available large servers without worrying about Java stuttering or stalling, said Howard Green, Azul vice president of marketing, in an interview.

For example, this capacity is useful for anyone who wants to hold all of their customer data or click streams in memory or retain a few days of pricing data. In-memory architectures give quicker access to data, Green stressed.

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Why open source is the ‘new normal’ for big data

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It’s no secret that Hadoop and Apache Spark are the hottest technologies in big data, but what’s less often remarked upon is that they’re both open-source.

Mike Tuchen, a former Microsoft executive who is now CEO of big-data vendor Talend, thinks that’s no coincidence.

“We’re seeing a changing of the guard,” he said. “We expect the entire next-generation data platform will be open source.”

The platform he’s referring to is an expanded Hadoop ecosystem, in which the whole stack is open source. “It’s the new normal,” he said.

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Jooby framework simplifies Java Web development

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When it comes to Web development, Ruby has the Sinatra framework and Node.js has Express.js. Now, Java has micro Web framework Jooby.

Built by developer Edgar Espina, Jooby offers modular, stateless application development leveraging NIO (non-blocking IO) servers including Netty, Jetty, or Undertow.

Oracle fixes critical flaws in Java, Database Server

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Oracle issued a gargantuan quarterly patch update this week, fixing a whopping 248 vulnerabilities across its product portfolio. Despite its size, Oracle Database, MySQL, and Java accounted for just a third of the fixes in the January Critical Patch Update.

The January CPU addressed seven vulnerabilities in the Oracle Database Server, three for the Oracle GoldenGate component, eight in Oracle Java SE, and 22 in Oracle MySQL. The update also closed nine issues in Oracle Virtualization and 23 in Oracle Sun Systems Product Suite, which includes Solaris. As has been the case with previous CPUs, the lion’s share of the fixes focused on Enterprise applications including Oracle EBS, Oracle Fusion Middlware, and Oracle PeopleSoft. All four patches with Common Vulnerability Scoring Standard scores of 8.0 or higher were for Java and Oracle Database.

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Bitbucket upgrade goes toe to toe with GitHub

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Giving GitHub a run for its money, Atlassian is upgrading its Bitbucket project hosting platform with improvements covering distributed teams, large files, and repository organization.

Supporting both the Git and Mercurial revision control systems, Bitbucket offers code collaboration and Git that “massively scales,” according to Atlassian. The company is adding Smart Mirroring, Git LFS (Large File System), and a Projects capability, for organizing multiple repositories, to Bitbucket.

“Git gives you by default whatever you want, and we help apply a consistent workflow,” Roger Barnes, Atlassian senior product manager, said in an interview. With Bitbucket, Atlassian sees “a huge opportunity for software teams to work faster,” he said. Bitbucket also integrates with the company’s Jira project management tool.

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9 hidden talents of devops ninjas

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Devops is all about culture, with groups of teams working in concert toward a common goal. But as opposed to some workplace cultures, there are certain traits and talents all devops team members must have in common. You could be the best software developer or system administrator in the world, but if you don’t possess “devops talents” you’ll soon find that you stick out like a sore thumb and any devops shop worth its salt will likely give you the boot.

High-performing devops shops fire on all cylinders, with each team member committed to the overall mission. When one team member doesn’t inherently believe in the mission, they can soon find themselves holding up the pipeline and becoming the bottleneck in what would otherwise be a successful deployment strategy.

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GitLab rev zeroes in on speed, search, and GitHub

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GitLab, a code-hosting alternative to GitHub, is adding performance monitoring and “super-powered” search to the 50th release of the platform. Version 8.4, unveiled late last week, also makes it easier to work with large files or large volumes of artifacts.

Performance monitoring gathers data on the time it took to complete a transaction, time spent running SQL queries, time spent executing Ruby methods, and system statistics like process memory usage. “We’ve said in multiple places that improving performance of GitLab.com is a big priority for us. To give us more insights into this, we’ve built performance monitoring into GitLab,” said Job van der Voort, GitLab vice president of product, in a blog post. “GitLab can now send performance data to an InfluxDB database, which in turn can be connected to graphing software such as Grafana.”

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Why developers should be allowed to roam free

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Most everyone now seems to subscribe to RedMonk’s “developers are the new kingmakers” thesis. Sadly, very few software companies understand how to take advantage of this power shift.

The confusion plays out again and again in the business and support models of otherwise developer-savvy software companies, which use bait-and-switch open source strategies to encourage adoption of and payment for their software. We used to call this “open core.”

Today, let’s call it “stupid.”

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Disgruntled devs vent GitHub grievances — on GitHub

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The people maintaining popular open source projects, from jQuery to TypeScript to Ember.js, have vented their frustrations about GitHub — on GitHub itself.

In the dear-github repository, more than 450 contributors to various open source projects have signed their names to an open letter identifying three key problems with GitHub.

The letter claims that despite repeated attempts to have GitHub address these issues over the years, the queries have been met with either “an empty response or even no response at all.”

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Go language expands to IBM mainframes

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Google’s Go, considered an up-and-coming language with usage in projects like Docker and Kubernetes, has netted another feather in its cap: a port to IBM’s z Systems mainframe platform.

The port was cited on a GitHub list of repositories from the Linux on IBM z Systems Open Source Team. While IBM’s mainframes are often viewed as legacy technology from years past, IBM wants to expand the horizons of its big iron systems with Go.

“We ported Go to z Systems as part of our overall effort to expand the platform’s open source ecosystem. We continue to look for ways to provide developers new options for taking advantage of the mainframe,” said Marcel Mitran, Distinguished Engineer and CTO for IBM LinuxOne, in an email.

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Why Spark 1.6 is a big deal for big data

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Big data got off to a roaring start in 2016 with the release of Spark 1.6 last week. You can rely on the Spark team to deliver useful features in a point release, but Spark 1.6 goes a step further, offering a mini-milestone on the way to Spark 2.0.

The new features and improvements In Spark 1.6 will make both developers and operators very happy. Let’s take a look at some of the highlights.

Automatic memory management

If you’ve talked to people who’ve used Spark in production, you’ll often hear them complaining about the hand-tuning required to optimize Spark’s memory management. In particular, you can spend days looking at garbage collection traces to tune the static split between execution memory (for shuffles, sorting, and shuffling) and caching for hot data memory locality.

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16 for ’16: What you must know about Hadoop and Spark right now

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The biggest thing you need to know about Hadoop is that it isn’t Hadoop anymore.

Between Cloudera sometimes swapping out HDFS for Kudu while declaring Spark the center of its universe (thus replacing MapReduce everywhere it is found) and Hortonworks joining the Spark party, the only item you can be sure of in a “Hadoop” cluster is YARN. Oh, but Databricks, aka the Spark people, prefer Mesos over YARN — and by the way, Spark doesn’t require HDFS.

Yet distributed filesystems are still useful. Business intelligence is a great use case for Cloudera’s Impala and Kudu, a distributed columnar store, is optimized for it. Spark is great for many tasks, but sometimes you need an MPP (massively parallel processing) solution like Impala to do the trick — and Hive remains a useful file-to-table management system. Even when you’re not using Hadoop because you’re focused on in-memory, real-time analytics with Spark, you still may end up using pieces of Hadoop here and there.

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Nodal: Build micro Web servers on Node.js

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The Nodal project is offering “Web servers made easy” via Node.js.

Currently in beta, Nodal is a Web server and framework built on a microservices architecture and intended for deployment of modular, distinct Web servers. It can be used for purposes ranging from a simple HTTP service providing server-generated HTML to an API server to a scaffold for single-page applications.

Projects can be deployed to the Heroku cloud out of the box. “Nodal servers are not meant to be monoliths,” the project’s GitHub pages states.

Keith Horwood, the developer of Nodal, stressed the technology’s simplicity and its use of Node.js, the popular server-side JavaScript platform. “Developers should get excited about Nodal if they’re interested in fairly simple server deployment, rigid design patterns, and idioms that hasten the conceptualization-shipment process. [Also, Nodal offers] the ability [to] bring new team members up to speed quickly and [leverages] the size and scope of the Node.js ecosystem,” said Horwood, an engineer at biotech engineering company Synthego. “Nodal uses modern ES6 syntax and will quickly familiarize any developer with changing JavaScript practices.”

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How tech giants spread open source programming love

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“Go is a programming language designed by Google to help solve Google’s problems.”  So said Rob Pike, one of the Go language’s designers.

That may be the case, yet the open source language is increasingly being adopted by enterprises around the world for building applications at large scale.

The story is similar with Erlang. Originally a proprietary language developed by Swedish telecommunications company Ericsson for handling massive volumes of telephone switching data on its hardware, Erlang was open sourced and is now rapidly gaining popularity for large-scale applications.

And there’s more. Facebook developed GraphQL and BigPipe technologies in response to the particular challenges it faces running a social network processing hundreds of billions of API calls a day for over 1.5 billion active members. Today these technologies have been open sourced, and are used by the likes of content management project Drupal to make its mobile web pages load faster.

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Jump into Java microframeworks, Part 3: Spark

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Spark makes fewer assumptions than the other microframeworks introduced in this short series, and is also the most lightweight of the three stacks. Spark makes pure simplicity of request handling, and it supports a variety of view templates. In Part 1 you set up a Spark project in your Eclipse development environment, loaded some dependencies via Maven, and learned Spark programming basics with a simple example. Now we’ll extend the Spark Person application, adding persistence and other capabilities that you would expect from a production-ready web app.

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The app dev undead: 5 technologies limping along

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Sometimes, technologies are introduced to great fanfare and then, well, fizzle.

For whatever reasons, they do not gain much momentum or get displaced by a theoretically better successor. Not all are inferior tools — in fact, they may even be superior to what was already available. However, superiority and innovation don’t always translate to successful adoption, though some establish cult followings.

The year, we’ve seen five such technologies aimed at developers fail to live up to their initial promise and thus fade away.

Famo.us: Rich Web tools can’t displace native apps

Last year, Famo.us was flying high and looked to give native mobile app development a run for its money. Equipped with a 3D physics-based animation engine, Famo.us positioned its open source JavaScript framework as taking Web development for mobile to the next level and simplifying the building of complex UIs.

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6 signs containers will gain ground in 2016

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With nearly every major IT product either adopting them outright or building in support for them, containers are guaranteed to continue changing IT through the coming year.

Here are six key ways that containers, and the ecosystem around them, will evolve and influence IT through 2016.

We’ll see more experimentation

The experiments in question don’t involve only using containers in places where they’ve never appeared before, but finding ways to further transform container technology itself.

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What machine learning will gain in 2016

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Machine learning is no longer some esoteric practice limited to mystical incantations by data scientists. It’s now a mainstream presence thanks to ubiquitous big data, and easier tooling and frameworks.

Here are four ways the machine learning landscape is likely to change over the coming year as it continues to both exert an influence on IT and be influenced by it.

Tech dinosaurs will continue to remake themselves around machine learning the way they did with the cloud

When the cloud first became the tech buzzword, it turned into a guiding light for all the old-school tech companies that were starting to look like they’d outlived their usefulness: HP, IBM, Microsoft. Now, machine learning’s turning into their next big savior.

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