Dashboards provide an enhanced view of your most critical business metrics. With the majority utilizing both real-time and historical data, they enable you to promptly respond to current trends as well as accurately forecast for the future. Also, reporting dashboards excel when compared to static reports, in regards to presenting data and objectives to stakeholders.

After an incredibly fast-moving 2023, what does the future hold for AI and search? Conversational generative AI leapt into the public consciousness over the past year, and organizations scrambled to define their strategy for capitalizing on the trend. AI-boosted relevance is reshaping the way users experience search — and elevating their expectations for the quality of the interaction.

Recently, I came across the quote, "The goal is to transform data into information and information into insights." This statement emphasizes the significance of data (logs) and the responsibilities associated with software applications for robust data management, particularly log management. Effective log management is essential for performance optimization, troubleshooting, ensuring strong security, and maintaining compliance.

If you’ve ever watched one of those rehabilitating a business show like Kitchen Nightmares, you know that the advice is often the same, “Don’t try to be everything to everyone. Pick what you’re good at and stick with it.” Essentially, the message is to pick your niche and be the best within it.

Artificial intelligence tools are everywhere, and it’s no mystery why. They can carry out a huge variety of tasks and find the solutions to many everyday problems. But these apps are only as good as their AI search algorithm. In simple terms, an AI search algorithm is the decision-making formula an AI tool uses to find the optimal solution to your specific problem. Search algorithms may make trade-offs between speed, relevance, or another weighted factor.

Observability is a key component of modern applications, especially highly complex ones with multiple containers, cloud infrastructure, and numerous data sources. You can implement observability in two ways: build your own observability solution or use a homegrown alternative like Coralogix.

First, when I mention Loki, I’m not talking about one of my favorite TV shows to binge-watch or the lead character played by Tom Hiddleston, who has arguably become one of my favorite characters in the Marvel universe. I’m talking about the Loki, which is a highly available, cost-effective log aggregation system that was inspired by Prometheus. While Prometheus is focused on metrics, Loki is focused on collection of logs.

Data lakes are everywhere! With data volumes increasing, cost-effective storage is becoming a greater need. With Cribl Stream, you can route data to an Amazon S3 data lake and replay or search that data at rest. But nothing is more frustrating than something not working and those blasted error logs that pop up. In this blog, some common errors for your S3 sources or destinations are highlighted, and some potential root causes and solutions are highlighted.

Preventing issues through proactive log analysis is more advantageous than reacting to problems with troubleshooting when they occur. Logs can act as a powerful source for proactive monitoring, and configuring the right alerts can ensure that you are notified about critical events in advance. In this blog post, we'll unveil a few suggestions for optimizing log-based alerting to enhance incident management and achieve operational excellence.

Log monitoring is a fundamental practice in the system administration and cybersecurity, playing a pivotal role in maintaining the health and security of computer systems. At its core, log monitoring revolves around the scrutiny of log files generated by diverse software applications, operating systems, and servers. These log files serve as detailed records, containing crucial information about system events, errors, and user activities.