Operations | Monitoring | ITSM | DevOps | Cloud

To recap from our first blog post, JFrog Workers is a service in the JFrog Platform that provides a serverless execution environment, similar to AWS Lambda services. The Workers Service is similar to the user plugins available in JFrog self-hosted, and can be used to perform tasks that extend the capabilities of the JFrog Platform according to your requirements.

Most SREs and IT Ops manage Java applications without source code access or communication with AppDev teams. When applications have performance issues those SREs or IT Ops teams deploying and maintaining the infrastructure often have to prove that it is the application at fault and supply information to the app supplier which provides evidence of the issue.

What is a Time Series Data Warehouse? How does it help in your monitoring journey? How does it differ from a Time Series Database? That and more.

Network traffic analysis is an important aspect of computer network management. Organizations can gain valuable insights into the behavior of their network infrastructure, identify potential security threats, and optimize network performance by monitoring and analyzing network traffic. Several network traffic analysis tools have been developed to aid in this process, each with its own set of features and capabilities. What Is Network Traffic Analysis?

Today’s networks support a lot of traffic data, especially with the adoption of embedded systems. Furthermore, the complexity of the data passing through networks has significantly increased. Yet system administrators have to manage and secure these networks effectively. A network protocol analyzer is an essential component of network security and management that every administrator needs.

Last week, I attended the Amazon Web Services (AWS) re:Invent conference in Las Vegas, NV, with 50,000+ others. It was quite a busy week with several keynotes, announcements, and many sessions. While the hot topic at re:Invent was generative AI, I’ll focus my blog post on a few customer sessions I attended around observability: Stripe, Capital One, and McDonald’s. ‍

In the realm of data and complex scenarios, we humans naturally gravitate towards visualizing things as entities with attributes, rather than just raw data. Consider the phrase, “The response time on our Ad Generation service has increased.” It immediately resonates with the audience supporting the service.

In my first blog post of 2023, I wrote about how difficult it seems to be for the average person to set and stick to their New Year’s Resolutions. Stats show that by mid-February, most people’s resolutions have been abandoned and have become a distant memory.

Threat vulnerability management, and managing your attack surface, are critical in the battle against cyberattacks. At some point before an successful attack, the internal process to manage threats and prevent access to sensitive data failed. How could they have done things differently? Were they just managing too much, too often, without the resources they needed?

In observability, finding the root cause of a problem is sometimes likened to finding a needle in a haystack. Considering that the problem might be visible in only a tiny fraction of millions or billions of individual traces, the task of reviewing enough traces to find the right one is daunting and often ends in failure.