The Linux command line provides some excellent tools for determining how frequently users log in and how much time they spend on a system. Pulling information from the /var/log/wtmp file that maintains details on user logins can be time-consuming, but with a couple easy commands, you can extract a lot of useful information on user logins.

In this article, we will be going over some tasks that a Linux administrator may need to perform daily related to user management.

It has long been recognized by the security industry that complex systems are impossible to secure, and that pushing for simplicity helps increase trust by reducing assumptions and increasing our ability to audit. This is often captured under the acronym KISS, for "keep it stupid simple", a design principle popularized by the US Navy back in the 60s. For a long time, we thought the enemy were application monoliths that burden our infrastructure with years of unpatched vulnerabilities.

So we split them up. We took them apart. We created micro-services where each function, each logical component, is its own individual service, designed, developed, operated and monitored in complete isolation from the rest of the infrastructure. And we composed them ad vitam æternam. Want to send an email? Call the rest API of micro-service X. Want to run a batch job? Invoke lambda function Y. Want to update a database entry? Post it to A which sends an event to B consumed by C stored in D transformed by E and inserted by F. We all love micro-services architecture. It’s like watching dominoes fall down. When it works, it’s visceral. It’s when it doesn’t that things get interesting. After nearly a decade of operating them, let me share some downsides and caveats encountered in large-scale production environments.

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And finally, there’s security. We sure love auditing micro-services, with their tiny codebases that are always neat and clean. We love reviewing their infrastructure too, with those dynamic security groups and clean dataflows and dedicated databases and IAM controlled permissions. There’s a lot of security benefits to micro-services, so we’ve been heavily advocating for them for several years now.

And then, one day, someone gets fed up with having to manage API keys for three dozen services in flat YAML files and suggests to use oauth for service-to-service authentication. Or perhaps Jean-Kevin drank the mTLS Kool-Aid at the FoolNix conference and made a PKI prototype on the flight back (side note: do you know how hard it is to securely run a PKI over 5 or 10 years? It’s hard). Or perhaps compliance mandates that every server, no matter how small, must run a security agent on them.

Kata Containers is an open source container runtime that is crafted to seamlessly plug into the containers ecosystem.

We are now excited to announce that the Kata Containers packages are finally available in the official openSUSE Tumbleweed repository.

It is worthwhile to spend few words explaining why this is a great news, considering the role of Kata Containers (a.k.a. Kata) in fulfilling the need for security in the containers ecosystem, and given its importance for openSUSE and Kubic.

Remember the one-size-fits-all approach to cloud computing? That was five years ago. Today, multi-cloud architectures that use two, three, or more providers, across a mix of public and private platforms, are quickly becoming the preferred strategy at most companies.

Despite the momentum, pockets of hesitation remain. Some sceptics are under the impression that deploying cloud platforms and services from multiple vendors can be a complex process. Others worry about security, regulatory, and performance issues.

What exactly is a container and what makes it different -- and in some cases better -- than a virtual machine?

Prior to the Red Heat deal, IBM was treading water. The company released earnings on July 17. For the second quarter of 2019, revenue was down year-over-year. Sales were $19.1 billion, down from $20 billion in the prior year’s quarter. The company’s Cloud and Business Services unit saw slight growth (5% and 3% YoY, respectively), but declines in the Global Technology Services and Systems units countered this improvement. Despite this slight revenue slip, IBM managed to keep quarterly operating income steady at ~$2.8 billion.

The Red Hat deal adds a variety of growth catalysts to the International Business Machines story. For one thing, the acquisition makes IBM a bigger player in the $1 trillion cloud computing space. The deal is expected to accelerate revenue growth and improve gross margins. The deal is also very synergistic. IBM can now sell Red Hat’s suite of solutions to their existing customer base. With IBM’s global reach, the company could expand Red Hat’s business better than Red Hat would have done as an independent company.

The Apache Software Foundation has published their 2019 fiscal year report highlighting their more than 350 open-source projects/initiatives and this also marks their 20th anniversary. 

The Apache Software Foundation's 2019 report values their code-base at more than $20 billion USD using the COCOMO 2 model for estimating. Though for their 2019 fiscal year the foundation turned a profit of $585k USD thanks to sponsors. There are more than 190 million lines of code within Apache repositories. 

I mean, just look at that! And this is just a start. Just as NodeJS’s creation sparked the explosion of endless JavaScript tools, the popularity of container technology started the exponential growth of cloud-native applications.

The good news is that there are several organizations that oversee and connect these dots together. One is the Open Containers Initiative (OCI), which is a lightweight, open governance structure (or project), "formed under the auspices of the Linux Foundation for the express purpose of creating open industry standards around container formats and runtime." The other is the CNCF, "an open source software foundation dedicated to making cloud native computing universal and sustainable."

In addition to building a community around cloud-native applications generally, CNCF also helps projects set up structured governance around their cloud-native applications. CNCF created the concept of maturity levels—Sandbox, Incubating, or Graduated—which correspond to the Innovators, Early Adopters, and Early Majority tiers on the diagram below.

Yesterday, the BlazingSQL team open-sourced BlazingSQL under the Apache 2.0 license. It is a lightweight, GPU-accelerated SQL engine built on top of the RAPIDS. ai ecosystem. RAPIDS. ai is a suite of software libraries and APIs for end-to-end execution of data science and analytics pipelines entirely on GPUs.

Explaining his vision behind this step, Rodrigo Aramburu, CEO of BlazingSQL wrote in a Medium blog post, “As RAPIDS adoption continues to explode, open-sourcing BlazingSQL accelerates our development cycle, gets our product in the hands of more users, and aligns our licensing and messaging with the greater RAPIDS.ai ecosystem.”

Aramburu calls RAPIDS “the next-generation analytics ecosystem” where BlazingSQL serves as the SQL standard. It also serves as an SQL interface for cuDF, a GPU DataFrame (GDF) library for loading, joining, aggregating, and filtering data.

A new open-source project wants to take analytics to the next level. BlazingSQL is a GPU-accelerated SQL engine built on the RAPIDS ecosystem. RAPIDS is an open-source suite of software libraries for executing end-to-end data science and analytics pipelines entirely on GPUs.

According to the team, BlazingSQL was built to address the expense, complexity and sluggish pace users deal with when working on large data sets.

“BlazingSQL addresses these customer concerns not only with an incredibly fast, distributed GPU SQL engine, but also a zealous focus on simplicity,” Rodrigo Aramburu, CEO of BlazingSQL, wrote in a blog post. “With a few lines of code, BlazingSQL can query your raw data, wherever it resides and interoperate with your existing analytics stack and RAPIDS.”

BlazingSQL enables users to query datasets from enterprise data lakes directly into GPU memory as a GPU DataFrame (GDF). GDF is a project that offers support for interoperability between GPU applications. It also defines a common GPU in-memory data layer.

With its roots and foundations in the open source Apache Cassandra database, Santa Clara headquartered DataStax insists that it likes to keep things open.

As such, the company is opening a wider aperture on its collaboration with VMware by now offering DataStax production support on VMware vSAN, now in hybrid and multi-cloud configurations.

Cockroach Labs, the New York-based developer of the open source distributed database project CockroachDB, today announced that it’s closed a $55 million, oversubscribed series C round co-led by Altimeter Capital, Tiger Global, and GV (formerly Google Ventures). The raise, which saw participation from existing investors Benchmark, Index Ventures, Redpoint Ventures, FirstMark Capital, and Work-Bench, brings the company’s total capital raised to $108.5 million and comes after a year in which revenue doubled quarter-over-quarter.