Operations | Monitoring | ITSM | DevOps | Cloud

Observability vs monitoring has been a common topic in DevOps recently. There has been a lot of debate, and I have learned a lot from them when I started my observability journey. Most literature on observability is associated with a particular product or shares a textbook definition. In this blog post, I want to give you a practical understanding of observability and the differences between observability and monitoring with different scenarios and examples. We will cover the following topics here.

Our Sr. Product Manager, Taylor Johnson, attended Cisco Live EMEA in Amsterdam last week, and here are his top five takeaways from this amazing event.

OpenSearch dashboards are a powerful tool for visualising and exploring data stored in an OpenSearch-compatible data store such as Elasticsearch. With OpenSearch's intuitive interface and advanced analytical tools, this visualisation tool makes it easy to gain insights into your data and monitor and alert upon key metrics. Throughout this article, we'll look at some of the most impressive OpenSearch dashboard examples that showcase it’s capabilities and versatility.

Mobile learning consists of learning on the go, via your smartphone or tablet. In a business setting, mobile learning offers the possibility for employees to follow their training wherever they are, with online courses and other content being accessible at all times. This learning method is available on many learning management system (LMS) platforms but, as we will see later, it is necessary to be vigilant in the choice of the latter because mobile learning comes with some technical constraints.

Around the world, thousands of DevSecOps teams are shipping software faster and more reliably by integrating Mattermost with their self-hosted GitLab deployments. When combined, these platforms connect developer toolchains with real-time communications, audio calling, screen sharing and collaborative workflows.

In today’s digital world, data is generated faster and at a larger scale than ever before. With software architectures increasingly adopting distributed, cloud-based models, network infrastructures have become complex webs of virtual and physical devices.

Data centers account for more than 2% of total global energy consumption. If action is not taken, it is estimated that data centers will be responsible for 8% of global energy consumption by 2030. Data center managers are under increased pressure to comply with corporate sustainability initiatives and dramatically increase their efficiency. To support their efforts, many data center professionals are turning to Data Center Infrastructure Management (DCIM) software.