Ubuntu 16.10 review: Convergence is in a holding pattern; consistency’s here instead
There's plenty in Ubuntu 16.10 that makes it worth the upgrade, though nothing about Canonical's latest release is groundbreaking. This less experimental but worthwhile update continues to refine and bug-fix what at this point has become the fastest, stablest, least-likely-to-completely-change-between-point releases of the three major "modern" Linux desktops.
Still, while the Unity 7.5 desktop offers stability and speed today, it's not long for this world. Ubuntu 16.10 is the seventh release since the fabled Unity 8 and its accompanying Mir display server were announced. Yet in Ubuntu 16.10, there's still no Unity 8 nor Mir.
NVIDIA GeForce GTX 1050 OpenGL/Vulkan/OpenCL Linux Performance
Earlier this week NVIDIA began shipping the GeForce GTX 1050 graphics cards and our first review is of a Zotac GeForce GTX 1050 Mini. A GeForce GTX 1050 Ti Linux review is still coming up plus some other articles looking at performance-per-Watt and other interesting areas for these low-cost Pascal-based GPUs. Here are results of the latest NVIDIA Linux performance compared to the latest open-source AMD Linux driver with various Radeon GPUs.
What you can learn from GitHub's top 10 open source projects
Open source dominates big data. So much so, in fact, that Cloudera co-founder Mike Olson has declared, "No dominant platform-level software infrastructure has emerged in the last ten years in closed-source, proprietary form." He's right, as the vast majority of our best big data infrastructure (Apache Hadoop, Apache Spark, MongoDB, etc.) is open source.
Day 2 operations are still dominated by manual and custom individual scripts devised by system administrators. Automation is needed by enterprises. Based on the above analysis, Ansible is a leading open source project with a high number contributions and a diverse community of contributions. Thus Ansible is a well supported and popular open source tool to orchestrate and manage OpenStack.
Databricks, a company founded by the creators of the popular open-source Big Data processing engine Apache Spark, is a firm that we've been paying close attention to here at OStatic. We're fans of the company's online courses on Spark, and we recently caught up with Kavitha Mariappan, who is Vice President of Marketing at the company, for a guest post on open source tools and data science.
Now, Databricks has announced the addition of deep learning support to its cloud-based Apache Spark platform. The company says this enhancement adds GPU support and integrates popular deep learning libraries to the Databricks' big data platform, extending its capabilities to enable the rapid development of deep learning models. "Data scientists looking to combine deep learning with big data -- whether it's recognizing handwriting, translating speech between languages, or distinguishing between malignant and benign tumors -- can now utilize Databricks for every stage of their workflow, from data wrangling to model tuning," the company reports, adding "Databricks is the first to integrate these diverse workloads in a fast, secure, and easy-to-use Apache Spark platform in the cloud."
Two OpenStack Foundation executives talk about what has gone wrong, what has gone right and what's next for the open-source cloud.
BARCELONA, Spain—When OpenStack got started in 2010, it was a relatively small effort with only two companies involved. Over the last six years, that situation has changed dramatically with OpenStack now powering telecom, retail and scientific cloud computing platforms for some of the largest organizations in the world.
Complex systems are intrinsically hazardous systems. While most web systems fortunately don’t put our lives at risk, failures can have serious consequences. Thus, we put countermeasures in place — backup systems, monitoring, DDoS protection, playbooks, GameDay exercises, etc. These measures are intended to provide a series of overlapping protections. Most failure trajectories are successfully blocked by these defenses, or by the system operators themselves.
Software-defined networking has matured from a science experiment into deployable, enterprise-ready technology in the last several years, with vendors from Big Switch Networks and Pica8 to Hewlett Packard Enterprise and VMware offering services for different use cases. Still, Nemertes Research's 2016 Cloud and Data Center Benchmark survey found a little more than 9% of organizations now deploying SDN in production.