Python has staked its claim as the most popular programming language among developers worldwide. Accessible via Windows, Linux, and Mac, it’s intuitive and easy to read, and its use of maths lends itself perfectly to Python for finance and data analysis.

Here at Cribl, we’re big on GoatFooding. We not only prepare but consume our own products, in our own products. Today we’ll pull back the curtains to shine a light on how we use Cribl products within our Cribl.Cloud service. Cribl is a pioneer in the observability space, so what better way to use our products than by observing Cribl.Cloud?

The traditional approach for searching observability data is a tried-and-true: Once all the search staging is accomplished, we can perform high-speed, high-performance, deep-dive analysis of the data. But is this the best way or even the only way to search all that observability data? The answer to the first question is maybe, as it depends on what you are trying to accomplish. The answer to the second question must be a resounding no.

Hi! We’re Splunk, and we’re glad you’re visiting us today. Honestly, we hear from people far and wide about “What does Splunk do?”, “Does the name Splunk mean something?” And of course, “How can I learn Splunk?” I wrote this article to help answer all these questions for you and point you towards whatever question you want answered.

There are many visualization types and configurations available to choose from. In general, keep your visualizations as simple and straightforward as possible to avoid distraction and highlight only the most important information. If there is too much unnecessary information on the page it can be overwhelming and focus can be misdirected to unimportant details.