There are a lot of reasons why people choose to shop at one online store over another or pick one streaming service over another from the type of service they are getting to pricing, quality and, you’ve guessed it from the title, speed. The speed to which I’m referring is the speed at which the website loads and reacts to user input. In one of my previous articles about netwok latency, I’ve talked about how big of a difference even a two-second extra delay makes.

For the newest instalment in our series of interviews asking leading technology specialists about their achievements in their field, we’ve welcomed Kathleen Moriarty, Chief Technology Officer at the Center for Internet Security. During her tenure in the Dell EMC Office of the CTO, Kathleen had the honour of being appointed and serving two terms as the Internet Engineering Task Force (IETF) Security Area Director and as a member of the Internet Engineering Steering Group from March 2014-2018.

When your Elasticsearch cluster ingests data, it needs to understand how the data is constructed. To do this, your Elasticsearch cluster undergoes a process called mapping. Mapping involves defining the type for each field in a given document. For example, a number or a string of text. But how do you know the health of the mapping process? And why do you need to monitor it? This is where mapping statistics come in. Mapping statistics give you an overall view of the mapping process.

It is commonly believed that once data is collected and ingested into a system of analysis, the most difficult part of obtaining the data is complete. However, in many cases, this is just the first step for the infrastructure and security operations teams expected to derive insights.

If you’re like most organizations, you’re leveraging Jenkins for all sorts of things. Deployment pipelines, automated API tests, even glorified CRON jobs just to name a few.

Shortly before the December holidays, a vulnerability in the ubiquitous Log4J library arrived like the Grinch, Scrooge, and Krampus rolled into one monstrous bundle of Christmas misery. Log4J maintainers went to work patching the exploit, and security teams scrambled to protect millions of exposed applications before they got owned. At Cribl, we put together multiple resources to help security teams detect and prevent the Log4J vulnerability using LogStream.

Legacy logging solutions simply couldn’t keep up with the complex, hyperconverged regional infrastructure at Civo, a Kubernetes service provider that enables users to launch k8s clusters within 90 seconds. “With our infrastructure and application deployment getting more complex and more distributed, we needed our logging solution and our entire observability stack to scale up with our needs,” said Anaïs Urlichs, Site Reliability Engineer at Civo.