Operations | Monitoring | ITSM | DevOps | Cloud

The second in our Feature Highlights webinar series, Protect the Business with Cribl Packs, highlights Packs and security use cases. Packs enable you to share complex Stream/Edge configurations across multiple Worker Groups/Fleets, between Stream/Edge deployments or with the Cribl Community. Packs roll up best practices to ensure Site Reliability Engineering (SRE) teams have the required data to protect the business.

Splunk builds innovative tools which enable users, their teams, and their customers to gather millions of data points per second from an ever-growing number of sources. Together, Splunk helps users leverage that data to deliver, monitor, improve, and secure systems, networks, data, products, and customers with industry-leading solutions and expertise.

We’re excited to announce that we’ve recently contributed Redis monitoring support to the OpenTelemetry collector. You can check it out here! You can utilize this receiver in conjunction with any OTel collector: including the contrib collector, the observIQ’s distribution of the collector, as well as Google’s Ops Agent, as a few examples.

Everyone at LogDNA is (unsurprisingly) obsessed with the power of log data. It is the single source of truth for what is happening in your environment and, when used correctly, provides the insights needed to deliver better experiences. Now more than ever, people across various teams understand the value of having easy access to log data within key workflows.

Whether it’s Apache, Nginx, ILS, or anything else, web servers are at the core of online services, and web log analysis can reveal a treasure trove of information. These logs may be hidden away in many files on disk, split by HTTP status code, timestamp, or agent, among other possibilities. Web access logs are typically analyzed to troubleshoot operational issues, but there is so much more insight that you can draw from this data, from SEO to user experience.

Being a Cribl Pack author, I frequently receive questions related to why I chose to implement a certain functionality inside my Packs the way I did. A few lives ago, I worked for a Fortune 250 oil & gas company where I managed our SIEM environment. We didn’t have much in terms of system resources, so we needed to make everything run as efficiently as possible. (Maybe that’s where I get my love for performance from?)

The world lives by processing the data. Humans process the data – each sound we hear, each picture we see – everything is data for our brain. The same goes for modern applications and algorithms – the data is the fuel that allows them to function and provide useful features. Even though such thinking is not new, what is new in recent years is the requirement of near-real-time processing of large quantities of events processed by our systems.

Inevitably, organizations that use technology (regardless of the extent) will have something, somewhere, go wrong. The key to a successful organization is to have the tools and processes in place to handle these incidents and get systems restored in a repeatable and reliable way in as little time as possible.

Tracking application-level and infrastructure-level metrics is part of what it takes to deliver software successfully. These metrics provide deep visibility into application environments, allowing teams to home in on performance issues that arise from within applications or infrastructure. What application and infrastructure metrics can’t deliver, however — at least not on their own — is breadth.