The "Q1 2020: Splunk Ideas" blog is officially live! This blog post is the first in a quarterly series that aims to educate and deliver status updates on "Splunk Ideas." In this post, I will cover the history and goals of Splunk Ideas and supply some information about our initial success. Next quarter’s post will focus on the lifecycle of an Idea, with details on our internal process of reviewing, considering, and prioritizing your ideas.

Innovation in hypervisor technology in the early 2000’s from both commercial and open source projects was the genesis for the public cloud as we know it today. Virtualization and Moore’s law, together with advances in storage technology, mobile and wireless, created a data explosion that continues to accelerate through today.

Company security usually depends on your ability to come up with a diverse set of passwords and then manage them. Remembering all of them is considered a tad too difficult for most mere mortals, so a number of password storage apps have emerged. But they too have to be secured, and ultimately results in inefficient access and flawed security. Single-sign on (SSO) is still preferred, but to make it effective, companies like Okta have to secure integration across a number of apps.

Like the sailor in Coleridge’s “The Rime of the Ancient Mariner,” who is surrounded by salt water that he cannot drink, many financial services professionals contend with similar challenges: data is all around them, but it’s not doing them much good. Firms need to drink deeply from their data, developing greater expertise not only at data discovery, but also at data valuation. Because at this point, data is the only true source of ompetitive differentiation.

Multiline logs provide valuable information for developers when troubleshooting issues with applications. An example of this is the stack trace. A stack trace is a sequence of method calls that an application was in the middle of when an exception was thrown. The stack trace includes the line in question that encountered the error, as well as the error itself.

Logging is critical for monitoring and troubleshooting your Node.js project. The open-source Winston logger helps take a load off our shoulders by making it easier to centralize, format, enrich, and distribute the logs to fit a particular need. Winston creates custom logger instances which can be configured to act as centralized logging entities. Essentially, the internal architecture of the module decouples the actual event logging from the implementation of the storage logic.

To achieve unified observability, we need to gather all of the logs, metrics, and application traces from an environment. Storing them in a single datastore drastically increases our visibility, allowing us to monitor other distributed environments as well. In this blog, we will walk through one way to set up observability of your Kubernetes environment using the Elastic Stack — giving your team insight into the metrics and performance of your deployment.

Data classification can be broadly defined as the process of organizing and tagging data by categories so that collected data may be used and protected in the most efficient way possible. Sumo Logic is an analytics platform that can ingest almost any type of machine data. This data can be structured or unstructured, come in the form of event logs and messages or as time-series metrics. Once the data has been ingested into the platform, it can be leveraged for a wide variety of use cases.

With most of the world on lockdown due to the COVID-19 virus, many aspects of IT services and digital transformation have been put into the fast lane. There are reports of massive surges in the use of tools such as Zoom, Microsoft Office 365, etc. in order to communicate and collaborate. At the same time organizations are required to scale up access to their internal applications.

Businesses who operate along supply chains, like manufacturers, distributors, and retailers, have innovative systems and processes for predicting demand and keeping consumers satisfied. Until a crisis hits. When demand for essential SKUs spikes due to panic buying and fear-based hoarding, organizations in essential consumer categories can find themselves forced to make critical decisions without reliable information to base them on, significantly increasing their risk exposure.