How much of the data you collect is actually getting analyzed? Most organizations are focused on trying not to drown in the seas of data generated daily. A small subset gets analyzed, but the rest usually gets dumped into a bucket or blob storage. “Oh, we’ll get back to it,” thinks every well-intentioned analyst as they watch data streams get sent away, never to be seen again.

Got cybersecurity problems? Well, the good news is the same as the bad news — you’re not alone. The world of security has a big data problem and an even bigger people problem. Enterprise connectivity has drastically increased in the last decade, meaning every employee, contractor, and vendor has some level of access to corporate networks. To support this growth, companies monitor exponentially increasing infrastructure and traffic, producing a steadily rising volume of data.

In this livestream, Ahmed Kira and I provided more details about the Cribl Stream Reference Architecture, which is designed to help observability admins achieve faster and more valuable stream deployment. We explained the guidelines for deploying the comprehensive reference architecture to meet the needs of large customers with diverse, high-volume data flows. Then, we shared different use cases and discussed their pros and cons.

In today’s contemporary landscape, organizations produce more data than ever, which needs to be collected, stored, analyzed, and retained, but not necessarily in that order. Historically, most vendors’ analysis tools were also the retention point for that data. Still, while this may first appear to be the best option for performance, we have quickly seen it creates significant problems.

Are you tired of sifting through data without context? Cribl Search adds valuable depth to your data, making it much easier to understand and analyze. No more squinting at cryptic logs or puzzling over unknown IP addresses! ️ Some common examples of how Cribl Search can enrich your data are adding service names or matching to threat intelligence. Another popular data enrichment is adding geographical location to events based on IP addresses.

Cribl’s integration catalog is ever-expanding. At Cribl, we constantly collect feedback on where to integrate next and channel it to deliver more high-impact integrations into our catalog. Whether it is Sources, Collectors, or Destinations, we constantly add new integrations to expand our reach in the IT security and observability ecosystem.

When exploring data, comparing individual data points with overall statistics for a large data set is often useful. For example, you might be interested in understanding when a performance metric rises above the historical average. Or possibly knowing when the variance of that metric increases past a certain threshold. Or maybe noting a change in the distinct number of IP addresses connecting to your public web portal.

This blog post discusses utilizing Cribl Search to pull and visualize data from the AWS API without ingesting data. This will allow you to collect, analyze, and visualize data from your AWS account in real time without ingesting the data first.

Cribl.Cloud has grown substantially since its launch, and our observability practice has developed in parallel. Gone are the early days of manageable logs and metrics. As we continue to grow, that problem will become even more challenging. We used Splunk internally, a well-used internal system, as our primary event management system. With Cribl Edge nodes deployed across our entire cloud fleet, we collect logs and metrics and send them to Cribl Stream for processing and routing.