As Kubernetes monitoring continues to standardize on Prometheus as a form factor, more and more developers are becoming familiar with Prometheus’ built-in query language, PromQL. Besides being bundled with Prometheus, PromQL is popular for being a simple yet expressive language for querying time series data. It’s been fully adopted by the community, with lots of great query repositories, sample playbooks, and trainings for PromQL available online.

MongoDB is a cross-platform NoSQL database that uses JSON-like documents with optional schema to store data. It was designed for high availability, high performance for high-data persistance use cases, and automatic scaling. Of course, all with the right infrastructure in mind. It is usually a good choice for document-oriented use cases when you need quick prototyping or massive scale. With the massive scale comes massive traffic, though.

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.

As high-scale Financial Technology (FinTech) companies build on their data-driven capabilities and establish product-market fit, they often experience rapid growth that creates challenges for IT and DevOps teams.

Operating a modern web application requires having a good handle on what’s going on with your application’s log data. But this doesn’t mean you need to keep the logs pulled up all the time. Sometimes, you want the flexibility to send important logged events to any number of places. The integration of SolarWinds® Papertrail™ and AWS Simple Notification Service (SNS) can help you make this happen.

So far in our series on scaling observability for game launches, we’ve discussed ways to 1) quickly analyze large volumes of telemetry data and, 2) ensure high-quality telemetry data for more effective analysis at lower costs. The best practices in these blogs outline best practices for scaling observability during game launch day – which is necessary to ensure high performance across all infrastructure components – to ensure no lag, no glitches, and no bugs.

Network monitoring is ideal for getting a real-time view of your connected environment, and with reports, you can look back in time too. Logs are key to this rear-view mirror look, as they contain all the data for all the elements you are monitoring. But without network log archiving, you can only look back so far. Did you know that according to an IBM/Ponemon study, it takes an average of 287 days to discover and contain a data breach?

IT systems are constantly growing in complexity. This makes it increasingly difficult to understand how a certain infrastructure-related issue would impact your application, end users and your business overall.

What is an observability engineer? Is it your SIEM admin? How about your application performance monitoring admin? Neither? Both? Observability engineering is more than administering a tool. There is more to it than data onboarding, writing parsers, and getting data in. As an observability tool admin, you work with data producers and consumers to get data in a human-readable and searchable format from the source to the analytics system.

HashiCorp Consul began as an open-source project for service discovery. It has evolved to provide other valuable functionality like secure service mesh to help secure microservice architectures based on service identity, but also the ability to achieve repeatable application deployment lifecycles via Network Infrastructure Automation and control access to the service mesh via Consul API Gateway.These features are considered the four core pillars of Consul service networking.