Automatic UI Updates (AUIU) is a new cloud service that allows admins to get the most up-to-date UI experience between Splunk Cloud upgrades. Cloud admins gain early access to newly enhanced self-service tools through the AUIU opt-in service. Specified AUIU enhanced pages and tooling can now be delivered to customers up to three months faster. AUIU is a delivery service that allows for new UI pages and UI improvements to be integrated into Splunk Cloud deployments for specific enhanced admin pages.

Dashboards are not just tools for businesses and other organizations to monitor and respond to their data, but can be a method of storytelling. All of our data has the potential to be crafted into compelling narratives, which can easily be accomplished with the help of Dashboard Studio’s customizable formats and advanced visualization tools. We can take a series of disparate datasets and bring them together in one place if they share a common theme — in this case, Halloween.

DevOps is an IT delivery concept that combines people, practices and tools with the shared goal of accelerating the development of applications and services. Adopting DevOps at enterprise level typically requires: The continuous development of DevOps practices, as well as other factors like the rapid pace of modern code changes, facilitates a need for DevOps monitoring: a set of tools and processes to support the entire software development lifecycle.

The Splunk App for Chargeback provides the ability to analyze and manage how internal business units, departments, and individuals are consuming Splunk resources. The three main features of the App are: The Splunk App for Chargeback allows you to manage, monitor, and forecast resource utilization in your shared Splunk environment. You can use Splunk App for Chargeback to focus on any business unit, department, or an individual user.

Software monitoring, how does it work? “We paid for a bunch of tools but we don’t know what we should be looking at. There are tons of charts that don’t seem to mean anything!” If you talk to people about software monitoring you’ve inevitably heard something similar to this. With so many possible metrics it can feel like searching for a needle in a haystack. Even with curated dashboards there is inherent confusion about what is important.

Splunk and Amazon Web Services (AWS) are celebrating 10 years of strategic collaboration — an incredible milestone which demonstrates our commitment to teamwork, co-innovation and exceptional, data-driven outcomes for our joint customers.

Moving to Splunk Cloud Platform has a lot of benefits, including flexibility, agility, and scalability. However, we understand that migrating to cloud is not a trivial task and can also bring up security concerns especially when it comes to having your data traverse the internet.

Every millisecond, humans generate significant volumes of data, from various IoT devices such as our wearable devices to daily activities such as internet surfing and tracking our workouts. Data continues to accumulate. Statista estimates that by 2025, the amount of data will have increased to 180 zettabytes. That's far too much information.

A typical bit of feedback I have had during my time at Splunk is that the Splunk Machine Learning Toolkit (MLTK) looks nice and all, but how are we supposed to get started using it? Choosing the right technique, let alone the right algorithm can be a daunting task for those who are unfamiliar with machine learning (ML). We’ve been thinking long and hard about how we can help offer more prescriptive introductions into using ML at Splunk and I’m pleased to present our set of MLTK deep dives.

Synthetic monitoring can be one of the most powerful tools in your DevOps team’s toolkit, especially for the SRE, yet is one that is often overlooked by people building out a reliability mindset. Synthetic monitoring permits you to simulate any transaction or interaction users can have in your website or app, from places around the world, as often as you’d like.