When you’re using Cribl Stream and Cribl Edge to send data to hundreds of Splunk indexers using Load Balancing-enabed Destinations, it is sometimes necessary to analyze memory usage. In this blog post, we delve into buffer management, memory usage calculations, and mitigation strategies to help you optimize your configuration and avoid memory issues.

My introduction to the world of data science was writing anomaly detection for a SIEM that catered to banks and credit unions. Some of these places were running on 50-year-old IBM core banking servers — meaning that someone trying to turn off a light in a server room could take down an entire bank with a literal flip of the wrong switch. While some companies take their time updating infrastructure, others still embody the move-fast-and-break-things philosophy of the early dot-com era giants.

It’s been nearly ten years since “The Ultimate CPU Alert – Reloaded” and its Linux version were shared with the SolarWinds community. At that time, managing CPU data from 11,000 nodes, with updates every five minutes to a central MSSQL database, was a significant challenge. The goal was to develop alerting logic to identify when a server was experiencing high CPU usage accurately.

The strategic implementation of a security log retention policy is critical for safeguarding digital assets and key company data. This practice is foundational for detecting and analyzing security threats in real-time and conducting thorough post-event investigations. Integrating the nuances of log analytics system costs, which escalate with data volume due to the infrastructure needed for storage and processing, highlights a critical aspect of security log retention.

IT and security teams have several products they use and in turn, have many admins. Some have wide privileges, while others have focused responsibilities for the various tools and touch points in an IT and security data path. Not all admins are members of all tools. But they are all typically part of a larger group bound by an email alias (aka a distribution list).

An analyst’s process often involves searching through a given set of data many times, refining the question and analytics performed each time. Cribl Search was originally designed to be stateless – executing each search from the original dataset provider(s) with every execution. However, a new feature has been introduced to allow searching into previous cached results, accelerating the analyst process for certain types of iterative search development.

Data is born at the edge, and the traditional approach is to collect it, then ingest it into one or more systems of analysis — or at least as much as you can afford to. And now the deep dive analysis begins. This might be the perfect solution for some datasets, but what about all the other data being collected on the edge? All the logs, metrics, and state information you seldom (if ever) retrieve?

What might you accomplish if you could easily search your data lakes without paying to move the data first? The most likely outcome is that you address a critical security incident quicker than ever, save your organization millions of dollars, get a promotion, and then go down in history as the best-looking, most talented analyst to have searched a storage bucket.

On a scale from walk-in-the-park → scaling Mount Everest, how easy is it for you to search your S3 buckets? Retrieving data to respond to security incidents, demonstrate compliance, or extract insights shouldn’t require jumping through hoops or overpaying for access.Cribl Search has native support for platforms like Amazon Security Lake, Amazon S3, Azure Blob, and Google Cloud Storage, enabling seamless data analysis right at its source.

Organizations today face a growing list of obstacles as they try to improve their detection, coverage, and accuracy. For one, data proliferation is happening at an astronomical rate. When was the last time your network bandwidth went down? What about your license costs for data storage or your SIEM? Difficulties arise from overlapping and poorly integrated tools that generate disparate data streams and several operational efficiencies.