It may seem like ancient history, but there was a time when telecommunications companies only had to worry about connecting customers over landlines. Today, their businesses depend on vast cellular networks to not only provide strong wireless phone coverage in countless locations, but also handle the demands of tablets, computers, and machine-to-machine communications.

Organizations are adopting cloud native and multi-cloud architectures to drive innovation, achieve faster time to market, improve yield, and deliver exceptional experiences to their customers. However, for all the business benefits of modernizing, the process does not come without challenges.

In Part I in our series outlining best practices for scaling observability, we reviewed the data analysis capabilities that can help engineers troubleshoot faster during high pressure situations during a game launch. Nobody wants lag time or crashes in their game launch. Similarly, no one wants terminated sessions or for your gamer customers to log off and play a competitor’s game.

We’re adopting Honeycomb with our teams, however, we’re trying to set up Availability Checks for our services like we’ve done with previous providers. How do we do that in Honeycomb?

Level 4, Proactive Observability With AIOps, is the most advanced level of observability. At this stage, artificial intelligence for IT operations (AIOps) is added to the mix. AIOps, in the context of monitoring and observability, is about applying AI and machine learning (ML) to sort through mountains of data looking for patterns.

September is here, and that means many retailers have already begun preparing for the upcoming holiday season. One weekend in particular tends to be the real-life stress test that companies have come to develop a love-hate relationship with: Cyber Weekend. Or more specifically, Black Friday, Cyber Monday, and the weekend in between.

Observability is one of the most critical ways to improve visibility and control over complex software systems. If you've ever wondered how observability differs from monitoring, then this guide will explain some of the key differences between these two popular concepts.

In my previous blog, I discussed how continuous observability can be used to deliver continuous reliability. We also discussed the problem of high change failure rates in most enterprises, and how teams fail to proactively address failure risk before changes go into production. This is because manual assessment of change risk is both labor intensive and time consuming, and often contributes to deployment and release delays.