Operations | Monitoring | ITSM | DevOps | Cloud

Enterprises are getting fed-up with their existing system monitoring tools. Despite decades of investments in monitoring tools, many businesses fail to notice a problem in their digital services until a customer calls to complain about it. So, it’s no surprise that businesses are looking for better solutions, and this has sparked an increasing interest in observability, according to Gartner in its updated report, “Innovation Insight for Observability.”

Modern software systems are complex, with services distributed across data centers, in many zones, all around the world. Gone are the days when we managed individual servers dedicated to our organization, comfortable with the knowledge of the unique quirks of our setup. Now we rely on others to manage massive data centers where we borrow small slices of virtual space on shared hardware, traveling over shared networks, all in a system we call the cloud.

Another conversation that was recently had during our weekly field enablement office hours here at Shipa was “what exactly is a microservice?”. Certainly, a term that has been used to describe a paradigm and architectural practice. One can argue that the breaking down or decomposing of applications has been going on for decades and is not a monumental shift in computing. There are several definitions of microservices from a purist definition to a definition resembling a movement.

To embed or not to embed: That is the question. At least, that’s one of the questions that companies have to answer as they decide how to implement Site Reliability Engineering. They can either embed SREs into existing teams, or they can build a new, separate SRE team. Both approaches have their pros and cons. The right strategy for your company or team depends, of course, on your needs and priorities.

Data masking is a technique of hiding sensitive data from certain users who should not be able to view or access the actual values. This post will look at the reasons why you might choose to implement data masking and discuss both dynamic and static data masking techniques, and when it is appropriate to use each of these.

In this blog, we will be introducing the concept of High-performance computing (HPC) and HPC clusters. We will also introduce a few categories of practical workloads important in the HPC space. This blog is a part of a series of blogs on HPC where we will introduce you to the world of HPC.

Datadog Events provides customers with a data feed about their infrastructure and applications, delivering an up-to-the-minute history of activity such as code deployments, configuration changes, and triggered alerts. Events collects data from Datadog products and over 100 third-party integrations—including Docker, Jenkins, Kubernetes, Sentry, AWS CloudWatch, and Azure Service Health.

This is the second part of our EKS with Terraform series. In Part 1, we talked about Infrastructure as Code and its benefits. We also mentioned the tools we will be using. This time we will actually start using Terraform to create a VPC and EC2 instance.