Operations | Monitoring | ITSM | DevOps | Cloud

Cribl’s integration catalog is ever-expanding. At Cribl, we constantly collect feedback on where to integrate next and channel it to deliver more high-impact integrations into our catalog. Whether it is Sources, Collectors, or Destinations, we constantly add new integrations to expand our reach in the IT security and observability ecosystem.

Observability is a key pillar for today’s cloud-native companies. Cloud elasticity and the emergence of microservices architectures allow cloud native companies to build massively scalable architectures but also exponentially increase the complexity of IT systems.

In part 1, we looked at how the design plan of traditional monitoring technologies depended heavily on properties of the systems that were intended to monitor and then showed how those properties began to be undermined by an increase in complexity, an increase which can ultimately be captured by the concept of entropy. In this part, we will explore how increased entropy forces us to rethink what is required for monitoring.

When exploring data, comparing individual data points with overall statistics for a large data set is often useful. For example, you might be interested in understanding when a performance metric rises above the historical average. Or possibly knowing when the variance of that metric increases past a certain threshold. Or maybe noting a change in the distinct number of IP addresses connecting to your public web portal.

In the business world, shadow IT is a controversial topic. Gartner defines Shadow IT as any IT devices, software and services that are used outside or beyond the ownership or control of IT departments/ organizations. This includes: In a standard work environment, the IT department would be responsible for providing whatever IT solutions and work tools were needed across all business functions.

For this week’s installment of “The concise guide to Loki,” I’d like to focus on an interesting topic in Grafana Loki’s history: ingesting out-of-order logs. Those who’ve been with the project a while may remember a time when Loki would reject any logs that were older than a log line it had already received. It was certainly a nice simplification to Loki’s internals, but it was also a big inconvenience for a lot of real world use cases.

This blog post discusses utilizing Cribl Search to pull and visualize data from the AWS API without ingesting data. This will allow you to collect, analyze, and visualize data from your AWS account in real time without ingesting the data first.

As we continue to iterate and help organizations meet their observability goals, Logz.io is thrilled to announce we’ve earned over a dozen Winter 2023 G2 Badges for our Logz.io Open 360™ essential observability platform! G2 Research is a tech marketplace where people can discover, review, and manage the software they need to reach their potential. Here are the Winter 2023 G2 Badges we’ve taken home for Application Performance Monitoring (APM) and Log Analysis.

Financial services and financial technology (FinTech) companies often depend upon complex infrastructure to handle their financial data. Security and compliance are paramount for these organizations, for gaining full visibility into the health and performance of these services to guarantee security is essential.

Cribl.Cloud has grown substantially since its launch, and our observability practice has developed in parallel. Gone are the early days of manageable logs and metrics. As we continue to grow, that problem will become even more challenging. We used Splunk internally, a well-used internal system, as our primary event management system. With Cribl Edge nodes deployed across our entire cloud fleet, we collect logs and metrics and send them to Cribl Stream for processing and routing.