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.

At this point we are well past the third installment of the trilogy, and at the end of the second installment of trilogies. You might be wondering if the second set of trilogies was strictly necessary (we’re looking at you, Star Wars) or a great idea (well done, Lord of the Rings, nice compliment to the books). Needless to say, detecting anomalies in data remains as important to our customers as it was back at the start of 2018 when the first installment of this series was released.

Last year, International Data Corporation released its Data GlobalSphere Forecast, 2021-25, in which it outlined the projected 23% compound annual growth in data, leaping to 175 zettabytes of data globally. So the natural question becomes, what will the world do with that much data? And, more importantly, what can your business do with your data?

* Co-author: Alexander Okl, Sr. Partner Development Manager EMEA | Google Cloud at Splunk “The way we look at manufacturing is this: the strategy should be to skate where the puck is going, not where it is.” - Tim Cook, CEO, Apple Inc.* So where is the puck going for manufacturers in 2022 and beyond?

Splunk builds innovative tools which enable users, their teams, and their customers to gather millions of data points per second from an ever-growing number of sources. Together, Splunk helps users leverage that data to deliver, monitor, improve, and secure systems, networks, data, products, and customers with industry-leading solutions and expertise.

DevOps is a well established discipline. By now, most developers, IT engineers and site reliability engineers (SREs) have heard all about the importance of “breaking down silos” and achieving seamless communication and collaboration across all stakeholders in the continuous integration/continuous delivery (CI/CD) process — which extends from source code development through production environment management and incident response.

Inevitably, organizations that use technology (regardless of the extent) will have something, somewhere, go wrong. The key to a successful organization is to have the tools and processes in place to handle these incidents and get systems restored in a repeatable and reliable way in as little time as possible.

Tracking application-level and infrastructure-level metrics is part of what it takes to deliver software successfully. These metrics provide deep visibility into application environments, allowing teams to home in on performance issues that arise from within applications or infrastructure. What application and infrastructure metrics can’t deliver, however — at least not on their own — is breadth.