Operations | Monitoring | ITSM | DevOps | Cloud

Current cloud investments have been a boost for flexibility and productivity. The challenge is that there is a single point of failure with these platforms, requiring diversity of providers to reduce dependencies. The future of cloud computing will see effects from the ambitions and expansion of cloud providers themselves, as well as potential competition. This is especially true with Amazon, Google, and to a lesser extent Microsoft.

These days, “SIEM” (Security Information and Event Management) is all over the place. SIEM tools work by collecting data from multiple systems and noticing patterns in the data. This adds immediate value to the business by providing insights, security recommendations, and actionable intelligence. Despite being helpful tools for many companies, SIEM tools do have their drawbacks. This article will describe the four main ones and offer suggestions for how they might be overcome.

The large volumes of logs, metrics, and traces generated by scaling cloud environments can be overwhelming, but they must be collected to identify and respond to production issues or other signals showing business or application issues. To collect, monitor, and analyze this data, many teams choose between open source or proprietary observability solutions.

Computing environments are constantly changing. Back when an on-premises server hosted your work, your infrastructure and applications were easy to track. Now that you’re developing in the cloud, things are more challenging. You’re learning that each team within your organization uses a different monitoring tool. At this point, you may be wondering if it’s time to build your own monitoring solution with open source tools at its core that everyone can use.

Elasticsearch and MongoDb are the two most popular distributed datastores used to manage NoSQL data. Both of these technologies are highly scalable and have document-oriented design at the core. There are differences between the two technologies, however, and it’s important to understand these differences in order to choose the right one for your use case. This blog post will examine the differences between these two technologies in a number of critical areas.