Cribl Stream and Cribl Edge can send data to Splunk in several different ways. In this blog post, we’ll focus on the common scenario where you want to connect Cribl Stream’s Splunk Load Balanced Destination to many Splunk Indexers at once. (We’ll talk about Cribl Stream, but what we say applies to Cribl Edge, too.) Cribl Destinations settings default to reasonable values. Sometimes Cribl Support recommends changing those values for better results in a given situation.

Splunk had just celebrated its 20th anniversary. The business was growing. Customers were loyal. So why would we consider refreshing our brand? The answer is simple, if you aren’t growing, you’re declining. Just like people, brands need to adapt and grow so they stay relevant. For us, part of our growth was reaching new audiences and launching new products. Which meant as brand stewards, we needed to update our brand to better connect with these new opportunities.

Did you know you can deploy Tetragon and parse high-volume logs with Cribl Edge? It’s true! Tetragon integrates seamlessly with Cribl Edge. This combination enhances monitoring capabilities in Linux environments. Have your cake and eat it, too. With a combined Cribl and Isovalent solution, you can deliver deep insights into your workloads, optimizing for your specific operational requirements with zero loss of data fidelity.

The beauty of deploying SaaS-based applications is that you don’t have to worry about building the infrastructure, hiring engineers to maintain it, staying on top of upgrades or worry about application security. Indeed, these are some of the main benefits you get by using a SaaS offering. However, the world of software is full of trade-offs, so, what do you lose out on?

Are you managing a Cribl environment? We love that for you; you’re at the forefront of complex data orchestration. As the steward of this dynamic data ecosystem, you have to manage and optimize the flow of information from diverse sources. As data volumes grow, the struggle gets even more real. No worries, though. You’ve got Cribl Stream. Monitoring Stream is critical.

A while ago, we posted a blog that detailed how we imported over 4 billion chess games with speed using Python and optimized the code leveraging our Universal ProfilingTM. This was based on Elastic Stack running on version 8.9. We are now on 8.12, and it is time to do a second part that shows how easy it is to observe compiled languages and how Elastic®’s Universal Profiling can help you determine the benefit of a rewrite, both from a cost and environmental friendliness angle.

Observability strategies are needed to ensure stable and performant applications, especially when complex distributed environments back them. Large volumes of observability data are collected to support automatic insights into these areas of applications. Logs, metrics, and traces are the three pillars of observability that feed these insights. Security data is often isolated instead of combined with data collected by existing observability tools.

Observability Pipelines have become vital tools for DevOps and Security teams to manage, control, store, route, and optimize telemetry data analyzed by Security Information and Event Management (SIEM), Application Performance Monitoring (APM), and Log management platforms. These teams spend hours every week trying to fit an increasingly large volume of data into the same size box.

Performance optimization is crucial when developing user-centric applications. To achieve better performance, it is essential to maintain effective log management. Client-side (user) logging is vital in driving website traffic or increasing user engagement with your applications. After deploying an application or a web browser, client-side information, such as user behavior, events, and errors, is not stored by default.

Structured logging is a methodical approach to log management in software development, often utilizing JSON or key-value pairs. This method enhances the comprehensibility and analytical efficiency of log data, particularly in complex and distributed system environments. Unlike unstructured logs, which lack a defined format, structured logs adhere to a standardized layout, facilitating streamlined analysis and troubleshooting.