Operations | Monitoring | ITSM | DevOps | Cloud

Many organizations have made the shift to the cloud in recent years, and many more are planning to or are just starting their cloud migration journeys now. However, some organizations struggle to realize value from this move. The benefits of cloud are clear: it’s flexible, scalable, and has a low cost of entry. But cloud can also bring complexity—creating new interdependencies, more services to manage, and more data and signals to monitor.

I hate reinventing the wheel once I find a good setup. On top of that, I dislike searching for all the links I used to come up with the “ultimate setup” for different services. So, I decided to outline for myself (and for you of course) my default setup when I deploy on Elastic Cloud to set myself up for success and automate insight for the future. Most of my setup steps make monitoring accessible or automate various warnings to myself.

The pandemic upended business for many or at the very least cast a grim shade of uncertainty, so, as many took to working from home, they also were commissioned with cutting waste. Among the biggest sources of misspend in 2020 – cloud services. And remote work may have actually spurred the problem, as organizations migrate more applications to the cloud to support these workers.

When building a microservices system, configuring events to trigger additional logic using an event stream is highly valuable. One common use case is receiving notifications when errors are seen in one of your APIs. Ideally, when errors occur at a specific rate or frequency, you want your system to detect that and send your DevOps team a notification. Since AWS APIs often use stateless functions like Lambdas, you need to include a tracking mechanism to send these notifications manually.

This blog will explain the Azure Data Explorer, its core concepts, and a few of its significant features. As the data generated by enterprises is growing day by day, data strategists are looking for more effective techniques to handle a massive volume of diverse data. With all these requirements, there emerges a need for a service that can collect, store, and analyze data with optimal performance. Let us see how Azure Data Explorer addresses these requirements.

Automation does not only eliminate the mundaneness of performing repetitive tasks, it can also help reduce errors and mistakes. Good cloud infrastructure encourages cloud automation. That should be one of the main motivators of implementing it. As ubiquitous as the idea of cloud computing has become in the last decade, there is still a lot of demystification to be done. People still don't fully understand its potential. Yes, it can save you money and time but how?