Operations | Monitoring | ITSM | DevOps | Cloud

The main idea behind Kubernetes is to create a standardized approach to running containers in the cloud. Whether you’re running AKS on Azure or EKS on Amazon, your cluster should still behave in more or less the same way. But that’s not to say you’re locked in to doing things one way; Kubernetes still offers a lot of flexibility in many cases. This is what experienced engineers take advantage of when trying to optimize Kubernetes performance.

We’re surrounded by news of data breaches and companies being compromised, and the existential threat of ransomware hangs over just about every organisation that uses computers. One of the consequences is that we are hassled by an ever-increasing number of software updates, from phones and computers to vacuum cleaners and cars; download this, restart that, install the updates.

When a business considers switching to a new MDM for their remote support, they primarily evaluate potential solutions from a technological perspective. The organization’s IT team will compare the MDM’s features vis-a-vis others in the industry, weigh over its specs, and determine how an implementation would work for their business. While this comprehensive technology review is important, the problem is that most businesses stop there.

When adding new Checks in Checkly a number of locations are available to check your endpoints from multiple locations around the world. For most use cases this is more than enough to ensure your resources are online. However, these locations are outside of your network and are unable to check on resources deployed more securely inside your private network.

With more and more applications moving to the cloud, an increasing amount of telemetry data (logs, metrics, traces) is being collected, which can help improve application performance, operational efficiencies, and business KPIs. However, analyzing this data is extremely tedious and time consuming given the tremendous amounts of data being generated. Traditional methods of alerting and simple pattern matching (visual or simple searching etc) are not sufficient for IT Operations teams and SREs.

The adoption of AI/ML in financial services is increasing as companies seek to drive more robust, data-driven decision processes as part of their digital transformation journey. For global banking, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year. But productionising machine learning at scale is challenging.

Kubernetes is a powerful platform that comes with many features to help engineers run their applications more efficiently. However, as you gain more experience and deploy more workloads, you’ll inevitably start looking for ways to optimize Kubernetes performance. There are many ways to approach optimization. On one hand, you could work exclusively with the tools and configurations provided by Kubernetes itself; on the other, you could reap the benefits of third-party tools.