Operations | Monitoring | ITSM | DevOps | Cloud

The State of DevOps Report finds a clear link between documentation quality and an organization’s ability to meet its performance goals.

If you work with large amounts of log data, you know how challenging it can be to analyze that data and extract meaningful insights. One way to make log analysis easier is to normalize your log messages. In this post, we’ll explain why log message normalization is important and how to do it in Graylog.

Log Management tools are crucial for the security and performance of your IT infrastructure. With the right log management system, you can quickly detect and respond to any anomaly or performance issue. Presently, there are numerous log management platforms. Each with its own unique set of features and benefits. While most of these platforms offer industry-standard capabilities, what sets them apart from each other are the stand-out features, pricing, and overall user experience.

Organizations are leveraging log analytics in the cloud for a variety of use cases, including application performance monitoring, troubleshooting cloud services, user behavior analysis, security operations and threat hunting, forensic network investigation, and supporting regulatory compliance initiatives. But with enterprise data growing at astronomical rates, organizations are finding it increasingly costly, complex, and time-consuming to capture, securely store, and efficiently analyze their log data.

On June 28th I will be hosting a webinar, ‘The Fundamentals of Searching Observability Data’. So why should you attend? Because things have, and will continue to change in the way we manage the IT data collected across the enterprise. A recent study shows that enterprises create over 64 zettabytes (ZB) of data, and that number is growing at a 27 percent compound annual growth rate (CAGR). The scary part?

Change is hard. Instigating change across an organization can feel nearly impossible. Just ask any executive about a time when they tried implementing new rules or introducing new software across the company, and you’ll hear plenty of horror stories. While many of us know the pitfalls associated with making changes that impact multiple stakeholders, there are ways to do it successfully.

The basic goal of log management is to make log data easy to locate and understand so that users can identify how their services are performing and troubleshoot more quickly. Logging as a Service, or LaaS, takes log management a step further by providing a solution that seamlessly scales and manages your log data via cloud-native architecture.

Distributed microservices and cloud computing have been game changers for developers and enterprises. These services have helped enterprises develop complex systems easily and deploy apps faster. That being said, these new system architectures have also introduced some modern challenges. For example, monitoring data logs generated across various distributed systems can be problematic.

Papertrail has had the ability to alert on searches that match events for years, but what about when they don’t? When a cron job, backup, or other recurring job doesn’t run, it’s not easy to notice the absence of an expected message. But now, Papertrail can do the noticing for you with inactivity alerts. Papertrail inactivity alerts allow you to setup notifications when searches don’t match events.