Increasingly, the speed and scale of a business can be measured by the resilience and performance of its applications. That’s why organizations are opting to modernize legacy applications by rewriting them using cloud-native tools and platforms. A Gartner study found that by 2025, cloud-native platforms will be the foundation for more than 95% of new digital initiatives, compared to less than 40% in 2021.
For many IT organizations, having complete end-to-end visibility of their IT assets as well as a comprehensive understanding of integrations, dependencies and changing statuses is an ideal position rather than a reality. This is especially true for organizations with legacy on-premises systems spread across many environments. Those that were cloud native or successfully migrated to the cloud had the privilege of cloud tools to visualize individual environments.
As we at Splunk accelerate our cloud journey, we’re often faced with the decision of when to use logs vs metrics — a decision many in IT face. On the surface, one can do a lot by just observing logs and events. In fact, in the early days of Splunk Cloud, this is exactly how we observed everything. As we continue to grow, however, we find ourselves using a combination of both. This post lays out the overall difference in logs and metrics and when to best utilize each.
If there is one thing organizations can take away from the past few years, it's that they are far more vulnerable than they could realize before. From pandemics to critical supply shortages to widespread data breaches and natural disasters, businesses that don’t have plans in place to handle and respond to emergencies are at tremendous risk. As leaders plan for inevitable crises and disruption, interest in business resilience and continuity grows.
When monitoring your application performance or troubleshooting an issue in production, context is key. The more information available, the faster the prevention of or detection of a user impacting issue. Observability tools offer many different features, like code profiling, to help contextualize your data. In this post, I’ll discuss what code profiling is and show an example of how it works.
Splunk Hunk is the nickname of Splunk Analytics for Hadoop. Hunk is an app available in Splunkbase. It is great for exploring, analyzing and visualizing data in Hadoop and NoSQL data stores. Hunk offers a shortcut around the hard work of inventing and coding every inquiry in Hadoop. Hunk helps to create insights from big data, without the need for specialized skills, fixed schemas, or months of development.
Curious about Splunk® Universal Forwarders? This article will sum up what they are, why to use them and how the universal forwarder works. Importantly, we’ll point you to the very best tips, tricks and resources on using universal forwarders (and other ways) to get data into Splunk.
