During our March Cribl User Group livestream, Cribl’s own Eugene Katz covered some of the updates we made to our documentation on Architectural Considerations for deploying Cribl Stream. Topics included our guidelines for determining the ideal number of worker nodes, accounting for throughput variability, and preparing for system failure. The full video has more information on these and other things to consider when determining the right balance between cost and risk for your organization.

In this blog series, we’ll explore how Corelight and Cribl Stream work together to improve observability in Security Operations Centers (SOCs). In today’s rapidly changing threat landscape, it’s crucial to efficiently monitor and manage data for effective security operations. Corelight provides exceptional network visibility, while Cribl Stream gives you control and the flexibility to optimize data pipelines and gain valuable insights.

The common failure scenarios that occur in the cybersecurity world are typically assumed to be costs of doing business, but they’re actually more predictable and avoidable than you might imagine. Even if you’ve been lucky enough to avoid failed data sources or backups, a SIEM getting knocked offline, and other cybersecurity attack situations until now — in today’s day and age, they’re still inevitable.

Hey there, Cribl fans! We hope you’re ready to move into the second half of summer with a splash because we have some exciting news to share. Our latest product launch is all about enabling teams and multiple users to work together seamlessly while focusing on security, access control, and providing valuable data insights on demand. Who says you can’t have it all? Let’s dive right into the details!

Many of us remember the public service announcements asking parents if they knew where their kids were at night. They were ominous and a little alarming. Asking yourself the same question about your data can be equally as scary, but Cribl Edge can help.

As someone who admittedly gets bored easily, one of my favorite things about working for a company like Cribl is the huge amount of technologies in our ecosystem I get exposure to. Over time, I also get to observe trends in the market – it’s always so cool to see big upswings in adoption for various platforms and tech. One such trend I’ve observed over the last year is a noticeable uptake and presence in the market of Google Chronicle.

Event Hubs is Microsoft’s cloud-native real-time event streaming service. For Event Hubs to work, data must be pushed to or pulled from it. That is where Cribl Stream comes in. Event Hubs is a source and destination inside Cribl Stream and the control for how you route, shape, and transform your data from Event Hubs. But, one does not simply Stream into (or from) Event Hubs. There is a lot that goes into architecting an Event Hubs Source.

Psst, hey pal, would you like to buy a time machine? I am not talking about some H.G. Wells monstrosity where you somehow end up being chased by dinosaurs or become your own grandparent. But a time machine for your observability data. License costs and tool performance often keep organizations from ingesting all their data or require them to limit data retention time. Security incidents are often discovered long after these retention times are exhausted or require data that was never ingested.

Can you believe we’re already halfway through 2023? Time flies when you’re busy innovating and having fun (yet somehow also seems so slow when you are waiting for your next vacation!!). At Cribl, we’ve been hard at work releasing wave after wave of incredible new features and capabilities across our entire product suite.

On July 4th we celebrate. We celebrate freedom of movement, freedom of assembly, removal of excessive taxation, and much, much more. But what about digital independence? Removing the tyrannical yoke of control over your observability data. Authoritarian vendors restrict access and movement; they dictate proprietary formatting and even limit what can be commingled with your data, then apply enormous tax burdens (i.e. license fees) just to store your data.