One of the core features of Cribl Stream is our Replay capability. We pride ourselves on giving customers choice and control over their data. The ability to archive data in cheap object storage, and then providing the ability to reach into the same object storage is one example of this. It’s safe to say that S3 and AWS have become synonymous with the term object storage. It’s like a modern day Kleenex, or Band-Aid.
In Cribl Stream and Cribl Edge, you can operate on your observability event data in flight, all the way down to the field level. Instead of writing complex regex to wrangle JSON and other structured formats, use Cribl’s built-in functions and extensibility to get the results you want. You’ll see formerly complex situations become easier to address and manage over the long term. In this blog, we’ll cover two troublesome use cases.
Syslog is a very common method for transmitting data from network devices and open systems servers data to analytics platforms like Elastic and Splunk. As adaptable as syslog is, it still has significant constraints, which is a pain for most companies that lack the resources to scale their capability needed for syslog.
At this point, you already know how powerful syslog is (and if you don’t, check out “Introduction to Syslog”). But here’s the thing: Scaling your systems to consume high volume syslog is like fighting zombies. Weird unexpected behavior and no easy solutions. Before you fight zombies, though, you have to understand them. So, here are the challenges for scaling syslog one by one.
Syslog is an event logging standard that lets almost any device or application send data about status, events, diagnostics, and more. It’s commonly used by network and storage devices to ship observability data to analytics platforms and SIEMs in order to support and secure the enterprise. Syslog is an excellent lightweight protocol to get telemetry from small scale devices.
To keep Splunk running like a well-oiled machine, admins need to ensure that good data onboarding hygiene is practiced. Traditionally, this meant ensuring that your props.conf was configured with the “Magic 8” and that the REGEX in your transforms.conf was both precise and efficient.
A recent ESG/ISSA survey highlighted that security professionals are overwhelmed with competing proprietary data standards and integration challenges. Today’s security landscape often comprises dozens of tools, each with its own unique format. Even if the format is defined and widely adopted, like Syslog, implementations vary widely from tool to tool, or even from release to release for the same tool. How big of a problem are these differing data formats?
Technically speaking, observability offers visibility into the data being generated by your infrastructure devices, systems, and applications — but in reality, it offers the opportunity to see what’s happening, There’s no guarantee that you’ll get what you want; you have to set things up in a way that makes it possible for you to get the insights you need.
