No matter what roles we play, we’ve all been frustrated end users at some time or another. Sometimes it’s as simple as struggling to find the documentation we need, whereas other times, we’re sitting there doing nothing because we forgot our passwords and must wait for an administrator to reset them.

While IT pros know they need to monitor IT services, they also know it can be the most difficult part of their job. Traditionally, enterprises have cobbled together several disparate monitoring products to address all their monitoring needs – but there are often gaps. Within these gaps, issues are missed, and the possibility of proactive issue resolution becomes nearly impossible.

I recently attended a virtual event and heard the speaker comment, “Relational databases don’t scale.” To my ears, this is about as silly a statement as saying, “No one can eat 26 hot dogs in 12 minutes” right before Kobayashi shows up and eats 50. In my experience, relational databases scale when they’re placed in the hands of someone who knows what they’re doing. Just imagine if Kobayashi was your data architect!

Over the past several years, there has been a big focus on streamlining infrastructure operations. Many organizations have begun using DevOps principles in their software development and operations activities. We've also seen a new Ops in town recently: DataOps. Good things come from new methods to help teams collaborate across an organization.

Cloud adoption has grown in recent years. From organizations developing a cloud-native strategy to those focused on cloud migration, there are many different ways to consume the cloud. No matter the reason for choosing the cloud or how organizations get there, many overlook one critical piece of the puzzle: cloud asset management. Have you been frustrated by figuring out what you have deployed in what cloud? If so, you're not alone. Let's examine why this is so important.

For decades, network administrators have tried to combine and centralize compute power. And the premise behind cloud computing was to delegate your data storage, analytics, and computing intelligence to some remote data center instead of or in addition to these resources remaining solely on-premises. Nowadays, increased processing power is combining with increased demands on response time to reverse the process. This blog post will dive deeper into the resulting trend, called edge computing.

Today, organizations are moving toward distributed systems to provide top-notch application services. This trend has made both observability and monitoring essential practices. As observability and monitoring are often used interchangeably, many people tend to get confused. It’s crucial to understand the difference between the two to use the right approach for a given scenario.

Like many companies, Stack Overflow is trying to get out of the business of running our architecture in our own data centers; instead, we want to offload some of the more mundane parts of system administration to a cloud service offering like Azure. I’m going to cut to the chase for the purpose of this article and concede we’ve already decided on Azure for the majority of our infrastructure and, most importantly to me, our databases.

As a data professional, I often think about all the things around me generating data and consider where this data is ultimately being used. Have you ever taken a minute to consider exactly how much data is being generated at any point in time? I love collecting data and looking at ways it can be used and living in a “smart” home with appliances logging and reporting usage patterns certainly provides a lot of opportunities.