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.

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.

IT, DevOps, and security teams are figuring out the best ways to manage their complex, ever-growing, ever-changing environments. And one contributing factor to all the complexity is the rise of using multiple cloud services. One cloud service to manage is difficult enough, but adding more to the mix — each with its own interface and set of tools — makes everyone’s job significantly more difficult.

Any web-based business must have effective log monitoring in place to guarantee the efficient operation of its applications and systems. Tools for log monitoring are essential for error detection, performance analysis, and problem-solving. The top five log monitoring tools will be examined in this post, along with their features, prices, advantages, and disadvantages.

In Elasticsearch 8.8, we’re introducing the reroute processor in technical preview that makes it possible to send documents, such as logs, to different data streams, according to flexible routing rules. When using Elastic Observability, this gives you more granular control over your data with regard to retention, permissions, and processing with all the potential benefits of the data stream naming scheme. While optimized for data streams, the reroute processor also works with classic indices.