As the demands of our customers continue to rise, Splunk User Behavior Analytics (UBA) V5.3 now boasts an increased ingesting rate up to 160K EPS from Splunk Enterprise to a 20-node large deployment. This scalability improvement facilitates support for 750K user accounts, 1 million devices, and 64 data sources.

Splunk had just celebrated its 20th anniversary. The business was growing. Customers were loyal. So why would we consider refreshing our brand? The answer is simple, if you aren’t growing, you’re declining. Just like people, brands need to adapt and grow so they stay relevant. For us, part of our growth was reaching new audiences and launching new products. Which meant as brand stewards, we needed to update our brand to better connect with these new opportunities.

With every major Splunk Enterprise release, we level up your dashboarding experience so that you can visualize and take action on your data fast. In Splunk Enterprise 9.2, we are bringing the experience across Classic (SimpleXML) dashboards and Dashboard Studio closer together and weaving in Dashboard Studio features from the two most recent Splunk Cloud Platform releases. This blog post covers the major dashboarding features included in Splunk Enterprise 9.2.

Splunk Observability Cloud includes powerful features which automatically identify patterns within your data to surface trends. The resulting insights tell you why some customers aren’t getting an optimal experience from your application, and how you can improve it. Unlocking these features requires attributes to be included with your application traces. But how do you know which attributes are the most valuable for your application and business?

We have all been there. When you are trying to buy a ticket and the app crashes or loads the next web page when booking a holiday only to find it takes forever and appears to hang. Our frustration level increases and if it continues, we will exit and go elsewhere. With banking apps though, we won’t move straight away but repeated bad experiences here will be remembered and eventually will make us move.

Splunk’s recent update to its Machine Learning Toolkit (MLTK) is a good reason to spend a few paragraphs thinking through the links between Observability and machine learning. First, let us quickly review what Observability and machine learning are.

“Is it worth it?” is probably the most common question customers ask business architects and value advisors. Whether it’s a software deployment or process improvement, customers want to be assured that the effort and risk of a project delivers real value. That is the question people in my line of work spend their days trying to answer. In many cases, the answer is complicated and requires a great deal of experience to explain.

For centuries there have been many wise sayings on how to deal with disruptions and prevail amidst uncertain circumstances. Read on to learn how Splunk and Bosch Rexroth are building the next-generation factory to help manufacturers elevate their resilience and take advantage of new market trends and operating models.

In the previous blog, we introduced what makes a bad alert and how being able to simply customise and fine-tune your detectors is critical to creating great alerts. The first category of detectors in Splunk Observability Cloud that we dived into was the out-of-the-box offering called AutoDetect. Customising and subscribing to these detectors is a great way to get up and running straight away with industry best-practice alerts and bring down MTTx.

Effectively managing both public and private Splunk Apps across multiple Splunk environments poses a considerable challenge, demanding significant time and effort with the potential for tedious and manual tasks. Recognizing this complexity, the Splunk Cloud Service has been progressively introducing additional features and capabilities to streamline and simplify these intricate administrative responsibilities.