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“I need it now and I need it reliable.”
– ANYONE WHO HASN’T DEPLOYED APPLICATION INFRASTRUCTURE
If you’re on the receiving end of this statement, we understand you in the K8ssandra community. Although we do have reason for hope — recent surveys have shown that Kubernetes (K8s) is growing in popularity, not only because it’s powerful technology, but because it actually delivers on reducing the toil of deployment.
Amazon Web Services (AWS) is the world’s leading cloud computing platform. It provides Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) offerings. AWS services can provide organizations with on-demand computing power, storage, application services, and content delivery services.
This article is a continuation of a series of posts about our project named Portfolio Architectures. The previous post, Portfolio Architecture Examples: Healthcare Collection, begins with a project overview, introduction, and examples of tooling and workshops available for the project. You may want to refer back to that post to gain insight into the background of Portfolio Architectures before reading further.
The collection featured today is centered around architectures in the telco domain. There are three architectures in this collection and we'll provide a short overview of each, leaving the in-depth exploration as an exercise for the reader.
Part of my responsibilities as a Network Programmer early in my career included supporting an enterprise implementation of Novell NetWare. It was the mid-1990s, and Novell had a dominant hold on the network operating system market. I decided it would be in my best interest to become a Certified Novell Engineer (CNE). A few months and several intense examinations later, I obtained my very own version of this certificate:
The most demanding examination in the series focused on networking concepts and the OSI model, which provides seven layers for computer systems to use when communicating over a network.
This is the second in a series of posts examining patterns for using K8ssandra to create Cassandra clusters with different deployment topologies.
In the first article in this series, we looked at how you could create a Cassandra cluster with two datacenters in a single cloud region, using separate Kubernetes namespaces in order to isolate workloads. For example, you might want to create a secondary Cassandra datacenter to isolate a read-heavy analytics workload from the datacenter supporting your main application.
Infrastructure is one of the core tenets of a software development process — it is directly responsible for the stable operation of a software application. This infrastructure can range from servers, load balancers, firewalls, and databases all the way to complex container clusters.
Infrastructure considerations are valid beyond production environments, as they spread across the entire development process. They include tools and platforms such as CI/CD platforms, staging environments, and testing tools. These infrastructure considerations increase as the level of complexity of the software product increases. Very quickly, the traditional approach for manually managing infrastructure becomes an unscalable solution to meet the demands of DevOps modern rapid software development cycles. And that’s how Infrastructure as Code (IaC) has become the de facto solution in development today.
One of the hot keywords in the DevOps space is AppOps. As the DevOps ecosystem matures, the focus is shifting from automation and continuous delivery to enriching the developer experience. AppOps takes an app-centric approach to enable developers with self-service tools to develop, deploy, and operate applications on modern, cloud-native platforms. While we have seen a proliferation of great open-source tools to achieve parts of this goal in recent years, creating a seamless experience that spans over CI/CD, security, cost management, and observability remains a challenging task.
Devtron is an open-source tool that pulls together a number of popular components such as ArgoCD, Clair, external secrets, and minio to bootstrap a fully managed application delivery platform on Kubernetes. Underneath the hood, it leverages GitOps principles to create sample CI/CD pipelines, integrated with security scanning and observability tools via a slick application dashboard. For teams looking to adopt Kubernetes at scale, Devtron offers a quick way to provide developers and platform teams a way to onboard their applications onto Kubernetes without having to fumble with various YAML files and piecing together complex tools.
After an application is deployed to production, developers should lock down its underlying infrastructure to prevent accidental changes. Some of the common accidents that can affect the availability of an application in production are: moving, renaming, or deleting the resource crucial to the function of the application. You can use locks that prevent anyone from performing a forbidden action to avoid such mishaps.
Almost every resource in Azure supports locks, so you will find the lock option in the settings section of nearly all resources in the portal. For example, the following screenshot illustrates locks on resource groups:
A few months back, I read a really excellent (but pretty old) blog post that explained how to hack a toy called a Mind Flex to extract and analyze the data within it. At first, I couldn't believe that such a thing existed. I mean, sure — gimmicky toys have been around for ages, so I wasn't shocked that the toy claimed to read the user's mind. It's not uncommon to fake this kind of gimmick. But, the fact that the Mind Flex contains a real, legit EEG chip that read your mind seemed almost too good to be true. I wondered if it was possible to take this hack a step further. Instead of just reading the data, or using the data to "control" something else, what if I were to read the data while performing some task and see what the data reveals about my performance during that task? I would need to complete an activity with quantifiable data to properly compare the brain activity to the task results to see if my attention levels correlated to the task's success or failure. Deciding on the actual action to measure wasn't tricky. I am a pretty avid video game player and had recently been trying to think of a way to integrate my gameplay statistics into a project, so I surmised that the combination would be an intriguing one.
So I asked myself: "if I could hack the Mind Flex and wear it while playing Call of Duty, what would the data show?" Could I establish a relationship between cognitive function and video game performance? In other words, when I'm focused and attentive, do I play better? Or, when I'm distracted, do I play worse? Is there no connection at all? I wasn't sure if my tests would succeed, but I decided to find out.
API monetization is a great way to recoup your investment in your API programs. Without direct monetization, you’re dependent on other sources of capital to grow the program, such as other profit centers or venture capital.
If you’re not directly monetizing your APIs, you could be leaving money on the table. This can be especially true if you don’t have any limits in place and lean on the honor system.
The idea of running a stateful workload in Kubernetes (K8s) can be intimidating, especially if you haven’t done it before. How do you deploy a database? Where is the actual storage? How is the storage mapped to the database or the application using it?
At KubeCon North America 2021, I gave a talk on “How to put a database in Kubernetes” where I demystified the deployment of databases and stateful workloads in K8s. Basically, it boils down to a few key steps:
The footprint of Kubernetes is expanding rapidly in all industries. Many enterprises already operate multiple Kubernetes clusters in multiple regions to address the needs of global operations and reduce application latency for customers worldwide. You may already have a large number of Kubernetes clusters in on-premises data centers and a number of public cloud locations, possibly using several cloud providers to avoid lock-in.
Unfortunately, operating a distributed, multi-cluster, multi-cloud environment is not a simple task. Kubernetes is a relatively new technology. It’s hard to find staff with Kubernetes skills or to identify the best tools for multi-cloud Kubernetes management.
Digital innovation is not only evolving the product's features but also the way they are designed and manufactured. To remain relevant in the changing market dynamics, enterprises must continuously strategize ways to optimize operational efficiency and infrastructure costs, improve product quality, and reduce time to market to accelerate the overall growth of the business.
Enterprises are required to embrace digital transformation and innovate at a faster rate. However, to achieve this, it is essential to choose the right PLM software. With several PLM options available on the market, it can be a daunting challenge to choose the right PLM software that best fits your business needs. That's why we have picked two industry-leading SaaS PLM platforms: Oracle Fusion Cloud and Propel PLM, and we will compare the differences between both solutions so that you can make an informed decision for your next PLM investment.
GitHub Actions: Performs the build and test (Continuous Integration)
AWS CodeDeploy: Automates the deployment process to EC2 (Continuous Deployment)
The Get Started examples on the K8ssandra site are primarily concerned with spinning up a single Apache Cassandra™ datacenter in a single Kubernetes cluster. However, there are many situations that can benefit from other deployment options. In this series of posts, we’ll examine different deployment patterns and show how to implement them using K8ssandra.
From its earliest days, Cassandra has included the ability to assign nodes to datacenters and racks. A rack was originally conceived as mapping to a single rack of servers connected to shared resources, like power, network, and cooling. A datacenter could consist of multiple racks with physical separation. These constructs allowed developers to create high-availability deployments by replicating data across different fault domains. This ensured that Cassandra clusters remain operational amid failures ranging from a single physical server, rack, to an entire datacenter facility.
When new technologies arise, we first adopt them for their technical value. If that value proves out, then we reach the magic “crossing the chasm” moment: when a technology jumps to widespread adoption through proven business value and goes mainstream.
Some technologies, a very select few, make one more jump forward, however — from mainstream to existential imperative.
Before the Covid pandemic, I had the pleasure of visiting "Motor City" Detroit in November 2019. I met with several automotive companies, suppliers, startups, and cloud providers to discuss use cases and architectures around Apache Kafka. A lot has happened. Since then, I have also met several OEMs and suppliers in Europe and Asia. As I finally go back to Detroit this January 2022 to meet customers again, I thought it would be a good time to update the status quo of event streaming and Apache Kafka in the automotive and manufacturing industry.
Today, in 2022, Apache Kafka is the central nervous system of many applications in various areas related to the automotive and manufacturing industry for processing analytical and transactional data in motion across edge, hybrid, and multi-cloud deployments. This article explores the automotive event streaming landscape, including connected vehicles, smart manufacturing, supply chain optimization, aftersales, mobility services, and innovative new business models.
Does using the cloud make your business sustainable? Research suggests that it’s a greener choice.
By moving to the cloud, the e-commerce giant Etsy slashed its energy consumption by 13% (from 7330 MWh in 2018 to 6376 MWh in 2019), saving enough energy to power 450 households for a month.(1) However, migrating to the cloud doesn’t guarantee anything if you neglect to optimize your resource utilization over the long term.
