Operations | Monitoring | ITSM | DevOps | Cloud

A cornerstone of network observability is the ability to ask any question of your network. That means having an unbound capacity to explore the tremendous amount and variety of network telemetry you collect. It means seeing trends and patterns from a macro level, but it also means getting very granular to pursue any line of analysis of your data. Collecting information from flow records, SNMP, streaming telemetry, BGP, eBPF, and so on is indeed very important.

Using the cloud can be challenging. Everyone likes to tell stories about a surprise cloud bill they received and laugh, but let’s be honest: it’s happened to all of us. When dealing with complex cloud environments and cloud-native architectures, gaining visibility into the state of the environment is critical. Today, developers are looking to monitor how things are working and performing as part of every step in the software development life cycle.

Imagine being able to mimic a celebrity’s look with a screenshot. Users could use the image to quickly find clothing sold online that matches the style. But, this is not the search experience of today. Customers struggle to find what they need, and if they can’t, they'll leave. Some of them don't remember the name (keyword) of what they are looking for, but have an idea of what it looks like or the actual image.

For businesses and organizations that need reliable, secure servers to host data, applications, or services around the world, Linux is often the platform of choice. From powering websites to managing network infrastructure to running enterprise databases, there are countless reasons people have turned to Linux as their go-to server solution over the past few decades.

Meeting bandwidth capacity needs of customers is a crucial business objective for today’s providers. While both coarse wavelength-division multiplexing (CWDM) and dense wavelength-division multiplexing (DWDM) are modern forms of wavelength-division multiplexing (WDM) that effectively solve those increasing bandwidth needs by upgrading the utilization of new and existing fiber, they are each designed to tackle different network challenges.

On their own, enterprise applications and systems are not always straightforward. Writ large, they are complex, integrated environments, full of multiple data formats and structures. You spend a great deal of effort and time to define and maintain diverse data models among these integrated components. A Canonical Data Model helps reduce that burden significantly — by promoting a standard and consistent data model between connecting components. This article describes a few things to get you started.

Caches in Bitbucket Pipelines allows build dependencies and outputs to be stored and reused across many pipeline runs. By reusing work previously done in an initial build, subsequent builds can save time by reducing the need for fetching dependencies or performing expensive computations.

In modern times, building and publishing an application has become very easy with Cloud-based deployment. Users don’t need to worry about infrastructure-related challenges like availability, reliability, scalability, etc. The cloud providers are responsible for keeping the deployment flow simple and intact. Providing many advantages and coherence, the high cost incurred for such benefits is the downside.

In my first two blogs in this series, I looked at some of the obvious signs and not so obvious signs that it might be time to change your RMM. However, the reality is that most MSPs don’t want to change unless they really have to, and in many of the cases I’ve seen they’re prepared to let things be bad for way too long.

Effective troubleshooting and resolution of critical production issues require DevOps and R&D teams to utilize logging and observability. However, selecting the right logging solution can be challenging, given the wide range of available options and associated costs. Additionally, the strategy for logging usage should be tailored to the needs of different personas and use cases, such as DevOps engineers versus developers.