Operations | Monitoring | ITSM | DevOps | Cloud

Bandwidth demands per enterprise site continue to skyrocket, while additional pressure has been added to the WAN in the wake of the pandemic and shift to remote working, moving demand from well-connected offices to a scattering of disparate locations. Yet, while WAN bandwidth demands are increasing by orders of magnitude every year, budgets rarely keep pace and significant numbers of those ‘better connected’ branch locations experience network congestion during peak hours.

I am super excited to announce that our Preview Environment feature is GA (Globally Available) for everyone 🥳.

In any enterprise, IT or otherwise, infrastructure monitoring and management are extremely crucial. A drop in performance or a failure of a machine can lead to significant delays. For that reason, there’s a constant need for eyes on the overall infrastructure to ensure smooth operations. One of the best ways to gauge the overall health of infrastructure is by monitoring key performance indicators (KPI).

What a difference a year makes. Each year, data center, lab, and edge sites become more complex, more distributed, and more difficult to manage. Managers must stay up to date on the latest trends and advancements in data center management best practices and technologies to maintain uptime, increase the efficiency of capacity utilization, and improve the productivity of people.

A fundamental requirement for building a profitable product is finding ways to reduce costs and improve efficiency. When you’re operating in the cloud, it’s almost impossible to gain this insight without data. This is where data-driven software development comes in. Data-driven development is a software engineering approach that relies on data to guide the development process.

A key goal for any DevOps team is to shorten the software development cycle and provide continuous delivery of high-quality software. Instead of continuing to the next logical goal, continuous deployment, most companies stop here. Developed code reaches the testing phase automatically, then, successful testing triggers a manual acceptance step. Only then is the application deployed into production.