Operations | Monitoring | ITSM | DevOps | Cloud

Security and Site Reliability Engineering (SRE) teams often operate as separate entities within organizations despite sharing similar goals: keeping systems secure, reliable, and performant. Security teams focus on protecting systems from threats and ensuring compliance with regulatory frameworks. SRE teams concentrate on system reliability, performance optimization, and incident management.

Whether it's a boss fight freeze or a sudden disconnect in multiplayer, crashes break immersion and make your players mad. Debugging these issues across multiple platforms—each with its own error-reporting system—only makes things harder.

High tech companies that use their own solutions project confidence to their customers that solutions truly work. Many teams across Stackify use Retrace internally, and my time in customer support gave me great insights into how our customers relied on Retrace to ensure applications consistently delivered a great user experience.

Ever found yourself buried in endless log files, trying to piece together what went wrong? For DevOps engineers, log analysis isn’t just about debugging—it’s a crucial skill for maintaining reliable systems and catching issues before they escalate. In this guide, we’ll cover everything you need to know about log file analysis, from the fundamentals to the best tools available today.

If you’ve ever found yourself sifting through logs, metrics, and traces without a clear answer to why your app crashed at 2 AM, you’re not alone. Troubleshooting without the right tools can feel like chasing shadows. That’s where the right OpenTelemetry backend makes all the difference—bringing everything together and turning scattered data into a clear picture.

If you're new to DevOps, you’ve likely noticed that website logging plays a bigger role than it seems at first. It’s not just a routine task—it’s how you keep systems stable, troubleshoot issues, and understand what’s happening under the hood. A good logging setup captures what went wrong, when, and why—helping you fix problems faster instead of guessing.

We’ve always known the proactive monitoring and advanced analytics provided by Martello’s Vantage DX can save organizations time and money while getting more from their investments in Microsoft Teams. We recently set out to prove that by building a research-based cost model with the help of our friends, the expert consultants at Enable UC. The results of that study were even more compelling than we expected.

Load balancers play a key role in Amazon Web Services (AWS) systems by maintaining traffic distribution, detecting server issues, and redirecting client requests to available servers without any downtime. But, choosing the right AWS load balancer can be daunting, as it’s essential for optimizing your application performance and scalability. Depending on your use case, you may find that an Elastic Load Balancer (ELB) or Application Load Balancer (ALB) better suits your needs.

Given the current climate in the federal government, it’s critical that public sector IT leaders find innovative solutions to do more with less. That’s a real challenge for these leaders who must balance with current alert backlogs against their agency limited IT budget and resources. Everyday, more than a thousand alerts to track down and as response times are slowing and some incident managers are burning out.

AI costs haven’t been a major factor in cloud computing — until now. For example, AI demands massive data processing and storage, such as for training Large Language Models (LLMs) and generative AI. Additionally, AI workloads require parallel processing, which traditional instances struggle to handle — forcing companies to invest in specialized (and expensive) GPUs to get the job done.