Operations | Monitoring | ITSM | DevOps | Cloud

What is CI/CD?

CI/CD is a software development strategy which allows for faster development by introducing automation while still maintaining the quality of code deployed to production. Implementing a CI/CD pipeline not only promotes a safer deployment process but also improves the incident response process. CI/CD is broken down into multiple parts. The CI refers to continuous integration, meanwhile, the CD can refer to continuous delivery and/or continuous deployment.

How to use Lambda extensions with Sumo Logic to reduce operational overhead and improve performance

We are excited to partner with AWS to get real-time log data from AWS Lambda through the new AWS Lambda Runtime Log API and AWS Lambda Extensions. AWS Lambda Extensions enable us to more easily integrate into the AWS Lambda execution environment to control and participate in the AWS Lambda lifecycle and the AWS Lambda Logs API enables us to collect function, platform, and extension logs. Through this integration, Sumo Logic and AWS make it possible to reduce operational overhead and improve performance.

How to catch all exceptions in Python

One of the struggles developers face is how to catch all Python exceptions. Developers often categorize exceptions as coding mistakes that lead to errors when running the program. Some developers still fail to distinguish between errors and exceptions. In the case of Python application development, a python program terminates as soon as it encounters an unhandled error. So, to establish the difference between errors and exceptions, there are two types of errors.

Automating common ServiceNow-Microsoft workflows just got easier

IntegrationHub spokes accelerate ServiceNow-Microsoft workflow automation One of the features of the Now Platform Paris release is built-in Microsoft Azure Active Directory integrations that simplify common workflows ServiceNow Onboarding and Software Asset Management (SAM).

Air Quality Monitoring Made Easy with the InfluxDB Air Quality Monitoring Template

Air quality monitoring is important as poor air quality is responsible for an estimated 60,000 premature deaths in the United States each year, and annual costs from air pollution-related illness are estimated at $150 billion. Air quality monitoring can help track and guide action to reduce air pollution, which can cause short-term and long-term health effects for children, older adults, and people with heart disease, asthma, and other respiratory conditions.

How Netdata gets you from 0 to monitoring in minutes

Netdata is zero-configuration monitoring. It’s a principle that we’ve stood behind since the project’s beginning, when it was only our CEO Costa trying to solve a “painful, real-world problem,” and it’s one we stand by today. Our insistence on zero-configuration guides every product decision we make, every grooming process, and every React component our frontend teams design.

Welcome to Netdata's community repository: Consul, Ansible, ML

On our journey to democratize monitoring, we are proud to have open source at the core of both our products and our company values. What started as a project out of frustration for lack of existing alternatives (see anger-driven development), quickly became one of the most starred open-source projects on all of GitHub.

Why modern testing requires Chaos Engineering

Modern applications are changing, and traditional testing practices are no longer up to the task. Learn more about the changing landscape of QA and how Chaos Engineering provides the necessary framework for testing modern applications. Chaos and Reliability Engineering techniques are quickly gaining traction as essential disciplines to building reliable applications. Many organizations have embraced Chaos Engineering over the last few years.

Scaling Fleet and Kubernetes to a Million Clusters

We created the Fleet Project to provide centralized GitOps-style management of a large number of Kubernetes clusters. A key design goal of Fleet is to be able to manage 1 million geographically distributed clusters. When we architected Fleet, we wanted to use a standard Kubernetes controller architecture. This meant in order to scale, we needed to prove we could scale Kubernetes much farther than we ever had.