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.

Considering the scale of log data that modern cloud environments generate, it’s oftentimes prohibitively expensive to index all of it. For monitoring and logging, cost management is just as important as in other parts of the business. Whether sudden spikes of log data overwhelm databases or good business generates more activity in your environment, teams should anticipate and mitigate the steep costs that result from high log volumes.

Out of the box monitoring solutions are getting more and more difficult to implement. In addition, they may not have the features you need, and they’re always becoming more expensive. It’s no wonder you’re trying to create a cost-effective solution via infrastructure monitoring cost management on your own by using a combination of your on-site development talent and a few open-source libraries.

Monitoring began by using software agents to capture data from infrastructure, operating systems, and applications. These agents would collect metrics and events from these systems to understand the health of the underlying system and the applications. This is what infrastructure monitoring is today.

At the end of October 2017, Microsoft announced the release of Azure Kubernetes Service (AKS), its hosted version of Kubernetes. If you’re new to AKS and curious about how to get a proof of concept (PoC) set up in your environment, read on. In this AKS tutorial, you’re going to learn, step-by-step, how to get an Azure Kubernetes cluster built with AKS.

In a previous blog post I discussed the complexity of securing Kubernetes environments, and some best practices. Unfortunately, even if we follow best practices in setting up and configuring our system, we can never eliminate all the vulnerabilities.