As data keeps growing at incredible rates, it’s becoming increasingly difficult to store and monitor at a reasonable cost leaving you to cherry-pick which data to store. As developers are accustomed to integrating metrics within their logs and spans, this can result in poor monitoring & analysis, alert fatigue, and longer MTTR. Teams are left having to dig out the most relevant data, which results in missed trends and analysis.

In today’s data-driven landscape, every organization knows the immense value their data holds, but with the explosion of data from diverse sources, traditional data storage and management solutions are proving inadequate. Organizations are urgently seeking new ways to handle their data effectively.

Data volumes are soaring. Environments are increasingly intricate. The risk of applications and systems encountering breakdowns is sky-high, and the mean time to recovery (MTTR) for production incidents is moving in the wrong direction. Disruptions not only jeopardize critical infrastructure but also have a direct impact on the bottom line of organizations. Swift recovery of affected services becomes paramount, as it directly correlates with business continuity and resilience.

As the automation of processes and deployment becomes more prevalent in the manufacturing industry, the need for IT services grows further. The use of complex systems and technologies, such as AI and robotics has become the new normal for manufacturing organizations.

Observability goes beyond monitoring; it's about truly understanding your system. To achieve this comprehensive view, practitioners need a unified observability solution that natively combines insights from metrics, logs, traces, and crucially, continuous profiling. While metrics, logs, and traces offer valuable insights, they can't answer the all-important "why." Continuous profiling signals act as a magnifying glass, providing granular code visibility into the system's hidden complexities.

Logs provide valuable information that can help you troubleshoot performance issues, track usage patterns, and conduct security audits. To derive actionable insights from log sources and facilitate thorough investigations, Datadog Log Management provides an easy-to-use query editor that enables you to group logs into patterns with a single click or perform reference table lookups on-the-fly for in-depth analysis.

At Cribl, we’ve always prided ourselves on solving complex data challenges for our customers, but doing so with a bold spirit and a can-do attitude. Our journey with Ian the Goat as our mascot has been nothing short of incredible. Ian represented our agile and adaptable approach to solving complex data challenges. However, as we pivot towards tackling even bigger data puzzles for our customers, we believe it’s time for our mascot to reflect this evolution.

Your log records may be missing a key ingredient that unlocks the world of observability for your applications, infrastructure and services. If you're building a new application or enhancing an existing one, consider adopting the OpenTelemetry Logs Data Model's Log and Event Record Definition. Adopting this definition enriches your logs by adding additional data, making it easier to use them to correlate them with metrics and traces, in addition to XYZ.

In our recent webinar hosted by Bill Balnave, VP of Technical Services, and Brandon Shelton, our Solution Architect, we discussed how data's continuous growth and dynamic nature cause DevOps and security teams to lose confidence in their data. The uncertainty about the content of telemetry data, concerns about its completeness, and worries about sending sensitive PII information in data streams reduce trust in the collected and distributed data.

In cybersecurity, “Load Balancing Graylog with Nginx: The Ultimate Guide” is your reference guide. This guide helps to install Nginx. Imagine your Graylog, already proficient at managing vast log data, now enhanced with the Nginx load balancing capability to ensure peak performance. NGINX ensures your Graylog cluster isn’t over-taxed, similar to a well-organized team where work is evenly distributed.