Operations | Monitoring | ITSM | DevOps | Cloud

With the aim to cut down development cycles and time to market, increase the frequency of deployment and ensure high-quality software releases, it is no surprise that DevOps has come to be a popular approach to application development.

Gartner introduced the word ‘AIOps’ back in 2017, and ever since, the enterprises have been adapting to the various strategies to streamline their IT operations. AIOps is a successful venture because it meets the challenges and tackles the amount of data created in the tech infrastructures amid complex architectures.

We live in a world that becomes more connected with each passing day. Public cloud hosts like Amazon Web Services (AWS) provide platforms with a wide array of capabilities that quickly scale based on demand. As a result, we’ve seen an explosion of new applications and services that continue to change our daily lives for the better. Data is a critical component of all of these systems. They can ingest vast amounts of data, process or transform it, and then pass it on.

Loggly is a feature-rich log management platform that also offers a wealth of features including automated log summaries, custom derived fields, unlimited users, search & filters and email alerts. Loggly is often used to solve a variety of logging use cases including handling Meteor, Java, IIS, Docker and Apache logs.

OpenTelemetry enables Observability, and building observable systems requires you to understand the various ways in which they can fail. Jumping from one possible fix to another and one change to another without fully recognizing the impact on the system can be a significant hindrance to a successful customer experience. In this post, I’ll explain how to get started with OpenTelemetry to help you make your systems more observable.

Making IT operations simpler – which AIOps does by helping teams to make smarter, more informed decisions about complex monitoring and APM problems – is great. But what would be even greater is eliminating the need for IT teams to make decisions at all – a prospect known as NoOps. By automating application management to the point that human involvement is no longer necessary, NoOps offers tantalizing possibilities for the IT operations teams of the future.

Kubernetes monitoring involves tracking application performance and resource utilization across cluster components, such as pods, containers, and services. The goal is to gain visibility into the health and security of your clusters. Kubernetes provides built-in features for monitoring, including the resource metrics pipeline that tracks several metrics like node CPU and memory usage and a full metrics pipeline.

We’re excited to announce the release of a major update to the Google Cloud Python logging library. v3.0.0 makes it even easier for Python developers to send and read logs from Google Cloud, providing real-time insights into what is happening in your application. If you’re a Python developer working with Google Cloud, now is a great time to try out Cloud Logging! If you're unfamiliar with the `google-cloud-logging` library, getting started is simple.

Active Directory (AD) is a foundational element of any Microsoft Windows environment because of the part it plays in authentication, access management, account management, and authorization. To ensure the health and efficiency of your Active Directory, it’s crucial for you to engage in proper Active Directory auditing and reporting best practices. This article will detail essential Active Directory auditing best practices and provide recommendations for the best Active Directory auditing tools.