Operations | Monitoring | ITSM | DevOps | Cloud

Relational Databases vs Time Series Databases

Databases are often the biggest bottleneck when it comes to application performance. Over the years a number of new database designs have emerged to help with not only basic scalability and performance but also to help improve developer productivity and make building certain types of applications easier. That isn’t to say these new databases are magical — there are always trade-offs being made and certain things are sacrificed for gains in other areas.

Introducing Nexthink Infinity

Today is one of the most special days in Nexthink history. I personally believe that founding and growing a tech company is mainly about developing amazing technologies which have the potential to change how people work. With the launch of our new Infinity platform, I feel we are truly transforming how digital workplace teams get their jobs done—not only for themselves but for all the employees in their companies.

Setting Up and Tuning Amazon S3 as a Cribl Stream Destination

Everybody is starting to look more at object storage to deliver on data lake initiatives, and S3, specifically Amazon S3, is the gold standard for that. In addition, we’ve heard from many of you that setting up S3 as a destination is a must when starting with Cribl Stream. So in this article we’ll walk you through the setup.

How to deploy a React app to Kubernetes using Docker

The concept of containerization helps you run applications as lightweight virtual machines. As a web developer, setting up local development environments can be tiresome. However, using tools like Docker and Kubernetes gives developers an upper hand to quickly set up and deploy applications. This guide uses Docker to deploy a React app to Kubernetes.

Banner Health streamlines vendor management across the organization

Healthcare organizations face myriad risks, from data privacy and corporate compliance to medical malpractice and environmental safety. With more than 30 hospitals and numerous specialized facilities across six states, US nonprofit Banner Health had different vendor management processes across the organization. “I wanted Banner Health’s teams to work together efficiently,” says Cameron Nickerson, IT vendor management director at the health system.

Grafana alerts as code: Get started with Terraform and Grafana Alerting

Alerting infrastructure is often complex, with many pieces of the pipeline that often live in different places. Scaling this across many teams and organizations is an especially challenging task. As organizations grow in size, the observability component tends to grow along with it. For example, you may have many components, each of which needs a different set of alerts. You may have several teams, each with a different channel where notifications should be delivered.

Qovery V3: Advanced Settings Building the Path to Beta Testing

Right at the beginning of the summer was the launch of our console V3 in Alpha testing; as explained in this article, the main goal of this V3 was to solve the UX issues present in the V2; it's also fully open source and rewritten from scratch in React. We gathered many feedbacks, and our Frontend team is continuing to add every feature already available on the V2 to go from Alpha to Beta testing at the end of September.

InvGate Insight Update: Easier Access to Health Rules

We have a new update for our InvGate Insight users. If you’re one of them, you surely know that you can already see the health state of your assets. However, we’ve just added a more direct way for you to access the health rules details, especially those in warning or critical state. Keep reading to find out the new path to view the health rules of your assets in InvGate Insight!

Harness Continuous Observability to Continuously Predict Deployment Risk

In my previous blog, I discussed how continuous observability can be used to deliver continuous reliability. We also discussed the problem of high change failure rates in most enterprises, and how teams fail to proactively address failure risk before changes go into production. This is because manual assessment of change risk is both labor intensive and time consuming, and often contributes to deployment and release delays.