One of the most important steps in log management is parsing of the log files, which turns unstructured data into understandable information. Logs are broken down by pre-established parsing rules, making monitoring and operating system performance easier and facilitating real-time problem-solving of the event logs. A Data Breach Investigations Report emphasizes the critical role of human error in cybersecurity, noting that it is a factor in 74% of all breaches.

Log Analysis has been a beneficial practice for organizations for numerous years, and over these years it has continuously evolved. This has been in part driven by the increasing volume of logs that companies are required to monitor. Now, log analysis is shifting again, incorporating machine learning (ML) and artificial intelligence (AI) to assist data analysts in identifying system log patterns and anomalies.

Learn to monitor host metrics with OpenTelemetry. Discover setup tips, common pitfalls, and best practices for effective observability.

Snowflake is special for several reasons. To begin with, its architecture separates storage and compute, making it fast, highly scalable, and efficient. Snowflake’s cloud-native, SaaS, and serverless approach also means you don’t have to worry about provisioning servers on-premises. Instead, you just need a Snowflake subscription; their team will handle the handy work on your behalf. At CloudZero, we use Snowflake for these and several more reasons.

What’s the difference between an associative array and a data container in CFEngine? CFEngine has two ways in which structured data can be used, associative arrays (sometimes called classic arrays) and data containers. Let’s take a look at a simple data structure. Here we have two data structures, a_email an associative array and d_email a data container. The policy emits the JSON representation of each.

Autonomous observability for system monitoring and management aims to use GenAI and machine learning to automatically detect, diagnose and resolve issues. In conversations about cloud observability today, discussions often shift from “what’s possible” to “what’s practical.” Too often, these conversations highlight the shortcomings of current observability processes, tools and financial models.

Over the last few years, cloud-native has become a popular buzzword in the tech industry. There are cloud-native applications, cloud-native network functions, even cloud-native storage. But, like many buzzwords, the true meaning of cloud-native has itself become clouded by overuse.