Monitoring has been around since the dawn of computing. Recently, however, there’s been a revolution in this field. Cloud native monitoring has introduced new challenges to an old task, rendering former solutions unsuitable for the job. When working with cloud native solutions such as Kubernetes, resources are volatile. Services come and go by design, and that’s fine—as long as the whole system operates in a regular way.
In the context of logging, multiline logs happen when a single log is written as multiple lines in the log file. When logs are sent to 3rd party log monitoring platforms like Coralogix using standard shipping methods (e.g. Fluentd, Filebeat), which read log files line-by-line, every new line creates a new log entry, making these logs unreadable for the user.
BlackSky monitors the globe from space, the air, the ground, the internet, environmental sensors, asset tracking sensors, satellites in space, social media feeds, industrial IoT, and other sources too numerous to name. Once gathered in their Elastic-powered analytics engine, all of the data from these disparate sources is correlated, compared, and cleaned.
In 2019 multiple cities, hospitals and educational institutions in the U.S. were crippled by ransomware, including Baltimore, Atlanta, New York City, Regis University in Denver and Monroe University in New York. In the the last 12 months, the infosec community has seen these ransomware operators seriously upping their game (see Ryuk ransomware).
Out of the four basic computing resources (storage, memory, compute, network), storage tends to be positioned as the foremost one to focus on for any architect optimizing an Elasticsearch cluster. Let’s take a closer look at a couple of interesting aspects in relation to the Elasticsearch storage optimization and let’s do some hands-on tests along the way to get actionable insights. The storage topic consists of two general perspectives.
Do you like gems? Perfectly cut diamonds? Crystal clear structures of superior beauty? You do? Then join me on a 10 minute read about a quest for hidden gems in your data: graphs! Be warned, it is going to be a mysterious journey into data philosophy. But you will be rewarded with artifacts that you can use to start your gemstone mining journey today.
As everyone is taking proactive measures to stay healthy, organizations are increasingly having their employees work from home. At Splunk, we are focused on bringing data to every question, decision and action — and remote work for us equals Zoom for online meetings and workspaces. As our customers use Splunk for real-time data processing and analytics, they use our Splunk Mobile App (Android, iOS) when they need to take their dashboards on the go.
As WFH usage surges, an increasing number of users will be connecting from home laptops over the internet into a Citrix server that could be hosted in a data center. This will likely put strains on licensing, internet bandwidth and server performance hosting the Citrix environment. These are all key areas that Splunk can monitor with various add-ons.
Elastic Workplace Search is rooted in a simple, but powerful mission: bringing information, content, and files from every corner of your organization into a singular, centralized source of truth. Thanks to its portfolio of first-party integrations, Workplace Search easily connects to a variety of popular content platforms.
