What is an observability engineer? Is it your SIEM admin? How about your application performance monitoring admin? Neither? Both? Observability engineering is more than administering a tool. There is more to it than data onboarding, writing parsers, and getting data in. As an observability tool admin, you work with data producers and consumers to get data in a human-readable and searchable format from the source to the analytics system.
This article is the final installment in a series that demystifies observability. The first three focused on the history of observability, dispelling myths around observability, and what observability is and what it can offer. In this last article of the series (Check out part 1), I want to offer a complete definition of observability.
How easy is it to work with your security tools? So easy that you’re telling all your family and friends and you singing their praises from the occasional rooftop? Well, we sure hope so. Security tools, like any other tool, should help you save time, not waste it. Nobody would have invented a drill if screwdrivers were fast enough — but it’s also up to you to make sure you are using your drill and all the other power tools available in the modern world.
Everybody is starting to look more at object storage to deliver on data lake initiatives, and S3, specifically Amazon S3, is the gold standard for that. In addition, we’ve heard from many of you that setting up S3 as a destination is a must when starting with Cribl Stream. So in this article we’ll walk you through the setup.
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.
