There are many posts and sites comparing fonts for programming and they are all amazing articles. So why I repeated the same subject here? Since I always found myself lost in dozens of fonts and could not finger out which one was best for me. So today I tried many fonts and picked up the following fonts for you. These fonts are pretty popular and easy to get. And most importantly, all these fonts are FREE!
I ranked the fonts with the following metrics:
Whether similar characters are distinguishable, such as 0O, 1lI
Whether the font style (line width, character width / height) is easy to read
Along with the clear benefits to be gained from upholding the standards enforced by GDPR, PCI DSS, HIPAA, and other regulatory bodies often comes a shift toward a more compliance-centric security approach. But regardless of industry or regulatory body, achieving and maintaining compliance should never be the end goal of any security program. Here’s why:
Compliance does not guarantee security
It’s critical to remember that many—if not most—breaches disclosed in recent years occurred at compliant businesses. This means that PCI compliance, for example, has been unable to prevent numerous retailers, financial services institutions, and web hosting providers from being breached, just as the record-breaking number of healthcare data breaches in 2016 were suffered by HIPAA-compliant organizations.
Compliance standards are not comprehensive
In fact, this trend reinforces how compliance standards should be operationalized and perceived: as thoughtful standards for security that can help inform the foundations of a security program but are by no means sufficient. The most effective security programs view compliance as a relatively small component of a comprehensive security strategy.
Container systems like Docker are a powerful tool for system administrators, but Docker poses some security issues you won’t face with a conventional virtual machine (VM) environment. For example, containers have direct access to directories such as /proc, /dev, or /sys, which increases the risk of intrusion. This article offers some tips on how you can enhance the security of your Docker environment.
Docker Daemon
Under the hood, containers are fundamentally different from VMs. Instead of a hypervisor, Linux containers rely on the various namespace functions that are part of the Linux kernel itself.
Starting a container is nothing more than rolling out an image to the host’s filesystem and creating multiple namespaces. The Docker daemon dockerd is responsible for this process. It is only logical that dockerd is an attack vector in many threat scenarios.
The Docker daemon has several security issues in its default configuration. For example, the daemon communicates with the Docker command-line tool using a Unix socket (Figure 1). If necessary, you can activate an HTTP socket for access via the network.
Popular hacking Linux distro Parrot Security has upgraded to version 4.0, and comes with all the fixes and updated packages along with many new changes.
“This release includes all the updated packages and bug fixes released since the last version (3.11), and it marks the end of the development and testing process of many new features experimented in the previous releases since Parrot 3.9,” reads the company’s announcement.
Parrot Security OS 4.0 will ship with netinstall images to enable those interested to create their own system with only the bare core and software components they need. Besides this, the company has also released Parrot on Docker templates that allows users to quickly download a Parrot template and instantly spawn unlimited and completely isolated parrot instances on top of any host OS that supports Docker.
CERN really needs no introduction. Among other things, the European Organization for Nuclear Research created the World Wide Web and the Large Hadron Collider (LHC), the world’s largest particle accelerator, which was used in discovery of the Higgs boson. Tim Bell, who is responsible for the organization’s IT Operating Systems and Infrastructure group, says the goal of his team is “to provide the compute facility for 13,000 physicists around the world to analyze those collisions, understand what the universe is made of and how it works.”
CERN is conducting hardcore science, especially with the LHC, which generates massive amounts of data when it’s operational. “CERN currently stores about 200 petabytes of data, with over 10 petabytes of data coming in each month when the accelerator is running. This certainly produces extreme challenges for the computing infrastructure, regarding storing this large amount of data, as well as the having the capability to process it in a reasonable timeframe. It puts pressure on the networking and storage technologies and the ability to deliver an efficient compute framework,” Bell said.
Tim Bell, CERN
The scale at which LHC operates and the amount of data it generates pose some serious challenges. But CERN is not new to such problems. Founded in 1954, CERN has been around for about 60 years. “We’ve always been facing computing challenges that are difficult problems to solve, but we have been working with open source communities to solve them,” Bell said. “Even in the 90s, when we invented the World Wide Web, we were looking to share this with the rest of humanity in order to be able to benefit from the research done at CERN and open source was the right vehicle to do that.”
Using OpenStack and CentOS
Today, CERN is a heavy user of OpenStack, and Bell is one of the Board Members of the OpenStack Foundation. But CERN predates OpenStack. For several years, they have been using various open source technologies to deliver services through Linux servers.
“Over the past 10 years, we’ve found that rather than taking our problems ourselves, we find upstream open source communities with which we can work, who are facing similar challenges and then we contribute to those projects rather than inventing everything ourselves and then having to maintain it as well,” said Bell.
A good example is Linux itself. CERN used to be a Red Hat Enterprise Linux customer. But, back in 2004, they worked with Fermilab to build their own Linux distribution called Scientific Linux. Eventually they realized that, because they were not modifying the kernel, there was no point in spending time spinning up their own distribution; so they migrated to CentOS. Because CentOS is a fully open source and community driven project, CERN could collaborate with the project and contribute to how CentOS is built and distributed.
CERN helps CentOS with infrastructure and they also organize CentOS DoJo at CERN where engineers can get together to improve the CentOS packaging.
In addition to OpenStack and CentOS, CERN is a heavy user of other open source projects, including Puppet for configuration management, Grafana and influxDB for monitoring, and is involved in many more.
“We collaborate with around 170 labs around the world. So every time that we find an improvement in an open source project, other labs can easily take that and use it,” said Bell, “At the same time, we also learn from others. When large scale installations like eBay and Rackspace make changes to improve scalability of solutions, it benefits us and allows us to scale.”
Solving realistic problems
Around 2012, CERN was looking at ways to scale computing for the LHC, but the challenge was people rather than technology. The number of staff that CERN employs is fixed. “We had to find ways in which we can scale the compute without requiring a large number of additional people in order to administer that,” Bell said. “OpenStack provided us with an automated API-driven, software-defined infrastructure.” OpenStack also allowed CERN to look at problems related to the delivery of services and then automate those, without having to scale the staff.
“We’re currently running about 280,000 cores and 7,000 servers across two data centers in Geneva and in Budapest. We are using software-defined infrastructure to automate everything, which allows us to continue to add additional servers while remaining within the same envelope of staff,” said Bell.
As time progresses, CERN will be dealing with even bigger challenges. Large Hadron Collider has a roadmap out to 2035, including a number of significant upgrades. “We run the accelerator for three to four years and then have a period of 18 months or two years when we upgrade the infrastructure. This maintenance period allows us to also do some computing planning,” said Bell. CERN is also planning High Luminosity Large Hadron Collider upgrade, which will allow for beams with higher luminosity. The upgrade would mean about 60 times more compute requirement compared to what CERN has today.
“With Moore’s Law, we will maybe get one quarter of the way there, so we have to find ways under which we can be scaling the compute and the storage infrastructure correspondingly and finding automation and solutions such as OpenStack will help that,” said Bell.
“When we started off the large Hadron collider and looked at how we would deliver the computing, it was clear that we couldn’t put everything into the data center at CERN, so we devised a distributed grid structure, with tier zero at CERN and then a cascading structure around that,” said Bell. “There are around 12 large tier one centers and then 150 small universities and labs around the world. They receive samples at the data from the LHC in order to assist the physicists to understand and analyze the data.”
That structure means CERN is collaborating internationally, with hundreds of countries contributing toward the analysis of that data. It boils down to the fundamental principle that open source is not just about sharing code, it’s about collaboration among people to share knowledge and achieve what no single individual, organization, or company can achieve alone. That’s the Higgs boson of the open source world.
Conventional metrics of open source projects lack the power to predict their impact. The bad news is, there is no significant correlation between open source activity metrics and project impact. The good news? There are paths forward.
Let’s start with some questions: How do you measure the impact of your open source project? What value does your project provide to other projects? How is your project important within an open source ecosystem? Can you predict your project’s impact using open source metrics that you can follow day to day?
While all these factors are critical in building a comprehensive picture of open source project health, there is more to the story. Indeed, many metrics fail to provide the information we need in a timely fashion. We want to use predictive metrics on a daily basis—metrics that are correlated with, and that act as predictors of, the outcomes and impact metrics that we care about.
ICANN had wanted a year of grace to address WHOIS’s data privacy problems. They didn’t get it.
ICANN argued, “Unless there is a moratorium, we may no longer be able to … maintain WHOIS. Without resolution of these issues, the WHOIS system will become fragmented … A fragmented WHOIS would no longer employ a common framework for generic top-level domain (gTLD) registration directory services.”
Share your expertise and speak at Open Source Summit Europe in Edinburgh, October 22 – 24, 2018. We are accepting proposals through Sunday, July 1, 2018.
Open Source Summit Europe is the leading technical conference for professional open source. Join developers, sysadmins, DevOps professionals, architects and community members, to collaborate and learn about the latest open source technologies, and to gain a competitive advantage by using innovative open solutions.
As open source continues to evolve, so does the content covered at Open Source Summit. We’re excited to announce all-new tracks and content that make our conference more inclusive and feature a broader range of technologies driving open source innovation today.
Jupyter Notebooks allow data scientists to create and share their documents, from codes to full blown reports. They help data scientists streamline their work and enable more productivity and easy collaboration. Due to these and several other reasons you will see below, Jupyter Notebooks are one of the most popular tools among data scientists.
In this article, we will introduce you to Jupyter notebooks and deep dive into it’s features and advantages.
By the time you reach the end of the article, you will have a good idea as to why you should leverage it for your machine learning projects and why Jupyter Notebooks are considered better than other standard tools in this domain!
What is a Jupyter Notebook?
Jupyter Notebook is an open-source web application that allows us to create and share codes and documents.
It provides an environment, where you can document your code, run it, look at the outcome, visualize data and see the results without leaving the environment. This makes it a handy tool for performing end to end data science workflows – data cleaning, statistical modeling, building and training machine learning models, visualizing data, and many, many other uses.
Jupyter Notebooks really shine when you are still in the prototyping phase. This is because your code is written in indepedent cells, which are executed individually.
Kubernetes adoption is exploding, but hype aside, Kubernetes remains very new — and has a long way to go before it ever might become an integral part of most IT infrastructures.
In the meantime, many, if not most, enterprises and IT shops are just looking to get their feet wet as they enter this brave new world of Kubernetes. And before developers can begin to do their work on the platform; the admins, operations teams and/or DevOps must lay the groundwork to add traditional, yet vital data management components to the mix: persistent storage is a good example of a necessary component in a Kubernetes deployment, while it is not always easy to implement.
Adding Persistent to Stateless
A key demand enterprises should have is that developers should be able to store data in Kubernetes clusters without having to worry about how persistent storage is working under the hood.
