The New Stack Makers

With containers comes a level of agility never before experienced in the world of business. In seconds, administrators can roll out deployments for a multitude of services. And when those businesses need to scale those containers, they can turn to Docker Swarm or Kubernetes. But what happens when even a standard container workflow can’t keep up with the ever-growing demand of business? You’d be hard-pressed to find a more efficient means of deploying and scaling a container cluster than that found in Kubernetes. And although that may very well be true, it doesn’t mean the development cycle can’t be improved. Enter GitOps GitOps is a method of workflow that was conceptualized by “everyone who successfully did infrastructure-as-code … is the true creator of the concept of GitOps.” (according to Priyanka Sharma, Director of Technical Evangelism at GitLab). However, ask anyone in the know and they will immediately say that Weaveworks wrote the book on this new workflow.

There's a continued welcome evolution in the role of the developer — from code monkey to creative worker. It's a good way to motivate and retain some of the most in-demand roles, by making the job more creative and less tedious. But truly it's in response to our increasingly complicated and distributed systems. As much of these roles has to be automated as possible to allow developers and other tech roles to focus on more problem solving and systems resiliency. In this episode of The New Stack Makers, we talk to Jason Yee about two trends surrounding this evolution — the role of the developer evangelist and the role of progressive delivery. We've already written about the increasing popularity of the developer advocate or evangelist. It's an eclectic role that involves a mix of content marketing and public speaking, documentation writing, and customer support. For Yee, in this role for almost three years at monitoring and analytics service Datadog, it's all about community, helping developers "think about tech in a different way."

Playtech describes itself as the world’s largest online gambling software supplier. It has achieved monumental growth to bolster its leading position in gaming software by largely relying on the creativity of its DevOps teams around the world. Needless to say, givings its developers and operations teams such freedom could come at a price, as managing the data and input of such as widespread and far-reaching DevOps structure would, for many companies, end in chaos. In this episode of The New Stack Makers podcast, Aleksandr Tavgen, technical architect, for Playtech described how the Isle of Man-based company has succeeded in achieving what the company says is “full observability” of Playtech’s operations platform thanks largely to InfluxDB 2.0, Flux and the OpenTracingAPI.

For this episode of The New Stack Makers, TC Currie is joined by Dr. Eric Brewer, Google Fellow and VP of Infrastructure at Google where he’s the architect of Cloud Native systems at massive scale.  He’s also been a Computer Science Professor at UC Berkeley since 1994.   TC caught up with Dr. Brewer at the recent Service Mesh Days in San Francisco. “Istio at its core decouples developers from operations,” Brewer said. That decoupling lets both sides go faster and with more confidence. Traditionally, when developers are writing a service, they worry a lot about the API, what are the methods, how does it work? But when you’re deploying microservices, then you start need to think about other questions. What are the policies that are calling this service?  Does it have quota? Does it have a denial of service?  How does it get authenticated?  How is it secured?  All of these questions that are not about what the API does, but are operations pieces.

GitLab has been very busy recently expanding on its leading software repository manager for DevOps to underpin its customers' continuous integration/continuous delivery (CI/CD) efforts. GitLab is also a very transparent company and actively communicates lessons it has learned along the way firsthand to better support its customers. As a case in point, Priyanka Sharma, director of technical evangelism at GitLab who serves on the board of the Cloud Native Computing Foundation (CNCF), recently surprised many by describing in a recent The New Stack blog post how GitLab began its CD journey without first completely shifting its underlying IT infrastructure to Kubernetes first. “We’re in the process of moving to Kubernetes, and we need to deploy even more frequently than we have in the past as we increase the velocity of feature development,” Sharma wrote. “But instead of modernizing completely with Kubernetes and then starting CD, we have opted to push our existing CI/CD system to the limit by using our preexisting legacy tools — and a lot of smarts.” In this episode of The New Stack Makers podcast hosted by Alex Williams, founder and editor in chief of The New Stack, Sharma further discussed how GitLab has sought to not necessarily opt for necessarily the latest-and-greatest tools sets in its move to CD. Instead, the focus has been on finding the most direct path while maintaining the investments in and support of GitLab’s legacy systems. “We're now in a place where we will start slowly moving to canaries because now we're maxed out what we could do with this legacy system,” Sharma said.

We've written some about accessibility in terms of building apps and websites — although there's always more to learn and do — but what about accessibility and inclusion in the developer world? How can you best serve your full customer base when your customers are developers? How is designing for inclusion an essential part of the developer experience? These are the questions Pivotal Labs' Senior Designer Raquel Breternitz is trying to answer in this episode of The New Stack Makers. Why is inclusion so important to Breternitz? "We don't have to just make sure that someone can get there, and can understand it when they do, but we also have to find the ways in which folks are excluded and break those barriers down as well," she told The New Stack in a follow-up conversation.

Splice Machine brings SQL Back, said Monte Zweben, CEO at Splice Machine, but with a spectacular twist. It’s common for data infrastructure to reside on three separate platforms:  transactional databases, data warehouses, and the recently-added machine learning (ML) environment. Most artificial intelligence (AI) applications are built on historical data that has been loaded in to ML environments for data mining.  So you’re looking in the rearview mirror to figure out what has already happened, he said.  And that requires computational engines. Then, since you want to perform actions to make decisions in the moment, you need an operational data store, like traditional database management systems or a NoSQL data store, but people use these to make decisions in the application in the moment. Lastly is new ML algorithms, data science workbenches and modeling workflows.

It’s just easier — a greenfield startup founder picks and chooses the best technology platforms and software development tools that best serve the needs of the business model. For organizations with years of legacy software in place, the digital journey is often harder, yet inherently necessary to survive in today’s DevOps-centric world. In this episode of The New Stack Makers podcast hosted by Alex Williams, founder and editor in chief of The New Stack, ahead of the upcoming TC Sessions: Enterprise event, how organizations attempt to keep up with the disruption amid an explosion of new and powerful open source tools served as a central theme. On hand to discuss what is at stake were: Ron Miller, enterprise reporter at TechCrunch. Yvonne Wassenaar, CEO of Puppet. Shawn Wormke, senior director of Aspen Mesh.

What does Justin Bieber have to do with our relationships to, well, anything? If nothing else, his staggering 105.8 million followers on Twitter (at the time of this conversation) is a real challenge for data scientists. If you wanted to know how The Bieb’s assemblage relates to him and one another, you could never achieve it with an entity-first relationship model — you know, columns, rows and spreadsheets, or, as Wikipedia calls it, “a simple relational database implementation, each row of a table represents one instance of an entity type, and each field in a table represents an attribute type.” If you really want to begin to understand the reasoning behind The Beib’s broad fanbase, you’d need to organize data relationship-first. This is just one of the real-world examples Dr. Denise Gosnell offered regarding the importance of graph databases and the tech behind them that is shifting how we think about and organize data. The head of global Graph practice at DataStax and one of the world’s foremost researchers on graph theory, graph algorithms, graph databases, and applications of graph data across all industry verticals offered more business use cases as well, including the interrelatedness of your primary and secondary LinkedIn connections and the growing popularity of smart meters.

“There’s so much passion for using AI in ways that are beneficial,” Anna Bethke, Head of AI4 Social Good at Intel’s AI Products Group.  And she wanted to make it easier for others to get involved. Bethke’s team partners with companies and social justice organizations to give them access to Intel’s knowledge of deep learning skills and techniques, making it easier for them to mine social media and other data to filter out the noise. One of her first partnerships is with National Center for Missing and Exploited Children (NCMEC). Their mission is to find children who are being trafficked or in danger of being kidnapped and alert the local law enforcement so they can take action. They get hundreds of thousands of alerts a day from social media like Facebook and Twitter, which was completely unmanageable. There are three points of information NCMEC needs.  First, identify and prioritize which posts represent the most danger to the child.  Second, identify where the post is originating, which is often complicated by the use of multiple IP addresses and IP address obscuring.  Once the location is found, the legal jurisdiction needs to be identified so the information can be sent to the proper authorities (e.g., police or sheriff department).  But how do you find that information in the flood of data?

Artificial intelligence (AI) and machine learning (ML) have been under study for years. But what is new is the tremendous impact and overlap AI and ML are now having on application development. Among the benefits, we will continue to see applications based on massive amounts of data sets that make use of human brain-like neural network computing to perform tasks in minutes or seconds that previously required thousands of human hours to perform. On a more practical level, AI is used to automate some of the more rudimentary tasks in software production pipelines, while freeing up time for developers to focus on more creative and intellectually rewarding work. ML and AI fit are also increasingly leveraging the resources and ability to rapidly scale Kubernetes, and more specifically, Kubeflow offer. During a podcast from the recently held from KubeCon + CloudNativeCon China, Alex Williams, founder and editor in chief of The New Stack, spoke with Dr. Han Xiao, engineering lead at Tencent AI Lab, and Alejandro Saucedo, chief scientist at the Institute for Ethical AI and machine learning, to learn more leveraging AI’s and ML’s power for today’s at-scale application development and deployments.

The Cloud Foundry Foundation may have faced some skepticism at the outset a few years ago, but it has since more lived up to its reputation as a as a principle hub for creating and leveraging open source software on the cloud. In many respects, Cloud Foundry has matured well beyond the initial phases of a startup, and with this maturity comes inherent challenges. Now, more than ever, Cloud Foundry must true to its core mission and to never “break the user,” Abby Kearns, executive director at the Cloud Foundry Foundation, said. During a podcast from the recently held from KubeCon + CloudNativeCon China, Kearns spoke with host Alex Williams, founder and editor in chief of The New Stack, about how the foundation continues to serve the developer open source community and what its missions continues to be. Kearns also revealed some especially interesting observations about China-based developers and their approach to the Cloud Foundry and open source community. Watch on YouTube: https://www.youtube.com/watch?v=Z155K1GuBjI

Inequality is growing. The climate crisis and global heating will only serve to increase this great human divide. All the while, technology is a huge contributor to both of these problems — from the massive carbon footprint and wasted electricity of data centers to mining limited resources to exploitation of factory workers to the continued lack of diversity and inclusion, there’s a lot of consequences to our supposedly forward-thinking industry. What are our responsibilities as people working in tech to curb these trends? In this episode of The New Stack Makers, we sat down with Nabil Hassein a technologist, educator and researcher into the historical link between computing and imperialism, to talk about what they call these “fractal patterns of inequality,” and how we examine and change the often damaging relationships within tech.

Kubernetes continues to spread across the cloud and to on premise and bare metal server environments — and with the wider scale adoption often comes growing pains. Specific to bare metal, for example, Kubernetes platforms lack viable load balancing capabilities. Beijing Yunify Technology, a China-based bare metal service provider, says it has solved the issue, with the development of open source  Porter designed to solve the issue of load balancing on bare metal in production on Kubernetes, During this The New Stack Makers podcast recorded live at KubeCon + CloudNativeCon Open Source Summit China 2019 in Shanghai, Xuetao Song, senior software engineer at Beijing Yunify Technology, described in more detail about how Porter offers load balancing capabilities for Kubernetes, while also touching on other container-related themes. Watch on YouTube: https://www.youtube.com/watch?v=zSWypFKaYcY

This episode of The New Stack Makers podcast talks to two Digital Ocean alumni and co-chairs of SREcon 2020 Americas who have had two very different journeys that led them to become one of the most wanted roles in tech — site reliability engineers. As the name suggests, an SRE is someone focused on the reliability of an organization's most important systems.  The term site reliability engineer dates back to 2003 when it was coined at Google, but it certainly has existed for decades more in different forms — disaster recovery and production testers, for example — as engineers have always tried to keep essential services like healthcare and finance online. The growing demand for SRE came as we went cloud-native and needed these engineers to work in production and on operations, with a heavy focus on automation and observability. As systems became increasingly distributed, this is a role that has evolved from just shoring up uptime for a monolith to a relationship broker who has views into organization-wide systems, a knack for problem-solving, and a love of metrics.