At first glance, availability monitoring may seem like one of the more mundane responsibilities of site reliability engineering (SRE) and IT operations teams. Determining whether an application is available may appear to be relatively straightforward, especially for teams that focus simply on monitoring certain transactions or services. This may have been true in the past.
Synthetic monitoring tools have long formed a core part of application performance management and monitoring toolsets. Yet no matter how familiar you are with synthetic monitoring, there is likely room to get more out of it than you currently are. Indeed, the default approach to synthetic monitoring tends to involve using it reactively: problems occur in production, and your team uses synthetic monitoring to help understand and remediate them.
It’s important for both technical and business teams to understand the different web performance monitoring options that are available as well as their various use cases and the benefits of each. In this article, we’ll compare synthetic monitoring and real user monitoring (RUM).
There are many different classes of web performance tools, from synthetic monitoring to application performance monitoring (APM), to real user monitoring (RUM), and more. These different classes exist because each has its own strengths and weaknesses. When evaluating open-source or enterprise-grade synthetic monitoring tools, you want to look for capabilities that maximize its strengths.
When I talk with Splunk customers, their challenges sometimes sound like trying to find a needle in a stack of needles. Feel the same way? The answers you need are out there, hidden in your data. Our job is to help you find them. Securing your networks, keeping them up and running and maximizing efficiency are key priorities. You also face the challenges of speeding up development and driving innovation to stay competitive.
In the late 1960s, there was a rock band called Ten Years After and I liked the name the first I heard about them. I wanted to use "Splunk and the Financial Services Industry: Ten Years After" as the title of this blog entry, but it’s been more than ten years since I wrote the first Splunk Blogs entry on Splunk and the Financial Services Industry. As you can tell, a lot has changed since then and more than a decade is an internet lifetime in technology.
Many CXOs believe that Web3 will power the next paradigm shift and transform the world. As a result, they are accelerating their learning curve to spot opportunities and leapfrog to next-gen business models that will catapult their organizations to new heights. But is there really an urgency to explore what Web3 can offer?
Splunk is all-in on OpenTelemetry, as exemplified by our native support for it in Observability Cloud, Splunk Enterprise and Enterprise Cloud’s usage of the OpenTelemetry Collector with Splunk Connect for OpenTelemetry Kubernetes, our long-term ambition to use OpenTelemetry as the main way that all Splunk Products capture data from customers’ infrastructure and applications for analysis, and our massive level of contribution to the project.
We are excited to share the latest advances around the Deep Learning Toolkit App for Splunk (DLTK). Earlier this year, Splunk’s Machine Learning Toolkit (MLTK) was updated with some important changes. Please refer to the blog post Driving Data Innovation with MLTK v5.3 and the official documentation to learn more about what changes were made and most importantly how they may affect you, especially if you run MLTK models in production.
