Has this ever happened to you: ‘I have too many agents to help me collect data for processing into separate SIEMs. It’s a pain to make any changes to their configuration!’ Or perhaps this one: ‘I have a large kubernetes deployment, but I just can’t seem to get metrics and logs out of it and into my SIEM or TSDB!’ Fear not, weary administrators, Cribl Edge is here!
A new vulnerability, CVE-2021-342 has been discovered in the Splunk indexer component, which is a commonly utilized part of the Splunk Enterprise suite. We’re going to explain the affected components, the severity of the vulnerability, mitigations you can put in place, and long-term considerations you may wish to make when using Splunk.
If you know anything about upholding the three pillars of observability for your business then you will know that centrally analysing and managing logs, metrics and traces is vital for improving how you observe the status of your business’s key infrastructure components.
Last week, Cribl launched the latest component of its observability architecture: Cribl Edge. ICYMI, Cribl Edge is a next generation observability data collector that greatly simplifies gathering your metrics, events, and logs. Edge incorporates all of the capabilities of Cribl Stream’s workers, allowing you to route, redact, filter, and enrich data directly from the source. Why is this important?
Today, Cribl is celebrating two significant milestones that are incredibly special to our founders and the entire company. Yesterday, Cribl celebrated its fifth anniversary, a day also shared with Clint’s son’s birthday. While we’re sure there was much celebrating (and cake!), it really earmarked the day our founders decided that building innovative software to help solve technology professionals’ most pressing problems was only going to happen if they were driving it.
In previous posts we covered why it’s important to monitor APIs and how to monitor and validate data from APIs. In this post we’ll focus on a simple but key feature that helps Splunk Synthetic Monitoring users create robust checks for availability, response time, and multi-step processes: Request Headers
In a recent episode of the Cloud Happens podcast, Archana Venkatraman, Associate Research Director in Cloud Data Management at IDC Europe talks about how the cloud isn’t a destination. It’s a continuum; a journey. In this blog, we explore that idea a bit more and dive into what really encapsulates a cloud experience. How can modern enterprises benefit from their cloud journey to solve the most gnarly data challenges to unlock innovation, enhance security, and drive resilience.
The key challenge with modern visibility on clouds like AWS is that data originates from various sources across every layer of the application stack, is varied in format, frequency, and importance and all of it needs to be monitored in real-time by the appropriate roles in an organization. An AWS centralized logging solution, therefore, becomes essential when scaling a product and organization.
Searching and visualizing logs is next to impossible without log parsing, an underappreciated skill loggers need to read their data. Parsing structures your incoming (unstructured) logs so that there are clear fields and values that the user can search against during investigations, or when setting up dashboards. The most popular log parsing language is Grok. You can use Grok plugins to parse log data in all kinds of log management and analysis tools, including the ELK Stack and Logz.io.
Users of open-source log collectors and log monitoring solutions often preferred these solutions due to them being well suited for speed, flexibility and their ability to attract talented contributors who are willing to invest time to maintain technology projects they are passionate about. In this post, we’ll look at some of the best free and open-source logging tools out there today.
