We are thrilled to be named a Leader in the Gartner Magic Quadrant for Enterprise Low-Code Application Platforms, based on Gartner’s evaluation of ServiceNow Now Platform App Engine (Orlando release). We believe this recognition validates our industry position and investment in the low-code application development space.
In this post we translate subqueries, using InfluxQL in InfluxDB version 1.x, into Flux, a data scripting and functional query language in InfluxDB version 1.8 and greater in either OSS or Cloud. The subqueries translated here come from this blog. This blog assumes that you have a basic understanding of Flux. If you’re entirely unfamiliar with Flux, I recommend that you check out the following documentation and blogs.
I’m excited to announce the launch of a new series of apps on Splunkbase: MLTK Smart Workflows. These apps are domain-specific workflows, built around specific use cases, that can be used to help you develop a set of machine learning models with your data. In this blog post, I’d like to take you through the process we adopted for developing the workflows.
While working with customers over the years, I've noticed a pattern with questions they have around operationalizing machine learning: “How can I use Machine Learning (ML) for threat detection with my data?”, “What are the best practices around model re-training and updates?”, and “Am I going to need to hire a data scientist to support this workflow in my security operations center (SOC)?” Well, we are excited to announce that the SplunkWorks team launched a new add-
The abundance of programming languages available today gives programmers plenty of tools with which to build applications. Whether long-established giants like Java or newcomers like Go, applications need monitoring after deployment. In this article, you will learn how to ship Golang logs to the ELK Stack and Logz.io. It’s usually possible to get an idea of what an application is doing by looking at its logs. However, log data has a tendency to grow exponentially over time.
Whenever you build a service and expose a set of endpoints to provide API access to that service, you’ll likely need to track their availability and response times, aside from ensuring their functionality. But to actually know that “something is down” or just “not performing” you need to consistently monitor your services day in day out and that’s how Heartbeat from the Elastic Beat family helps you with Uptime Monitoring.
ICYMI: We recently announced that Cherwell selected Resolve as their top partner for discovery and dependency mapping. The Resolve platform seamlessly integrates with Cherwell products to provide customers deep visibility into complex infrastructure and business-critical applications, as well as to ensure their CMDBs are always up-to-date and accurate.
According to Merriam-Webster, a mustache is 'the hair growing on the human lip' – so let's be clear that when referring to mustache pickers in this blog, we are not suggesting using tweezer-like tools to help you design hipster dashboards. Instead, we are talking about an awesome SquaredUp productivity feature called mustache picker that helps you get quick and effective results when using mustache template syntax within SquaredUp.
Something we get asked quite often is, how can I see the size of my Containers in each Storage Account? We know that Azure storage is one of the fastest growing aspects of Azure because it offers almost a limitless supply of storage, so companies are consuming more and more storage at an explosive rate. The question of being able to track this consumption is becoming more and more relevant. OK, so how can I get the size of my containers in a Storage Account?
Logging has been around since programming began. We use logs to debug issues and understand how software works at the code level. After logging and debuggers, profilers are a dev’s best friend when writing code and may run in production with limits to reduce overhead. As we distributed architectures — making systems more complex — centralized log aggregation was soon necessary. At that point, we had to analyze this data. Hence, log analytics technologies were born.
