Operations | Monitoring | ITSM | DevOps | Cloud

Our Big Tent philosophy provides the foundation for our broad, modular, and flexible observability platform. With Grafana’s powerful ability to integrate with a wide range of data sources, tools, and plugins, you can create customized solutions tailored to your unique needs.

For developers, the main quest is building and scaling their applications—not struggling with complex monitoring setups. Yet, observability in cloud-native environments is essential, and configuring monitoring for AWS services has traditionally been a complex and manual process. Developers had to set up Firehose streams, CloudWatch metric streams, and log subscriptions, all while ensuring continuous maintenance for new instances, turning observability into an unwelcome side quest.

Cardinality refers to the number of unique values in a dataset column. A column with many distinct values—like a user ID or timestamp—has high cardinality, while a column with limited distinct values—like a boolean flag (true/false) or a category with a few possible options—has low cardinality. For example, consider a database of an e-commerce platform.

Logs are the backbone of debugging, security, compliance, and performance monitoring. But if you don’t manage retention properly, you’ll either drown in unnecessary data or lose critical insights too soon. Log retention is all about striking a balance between keeping what’s necessary and discarding what’s not.

Syslog is the backbone of logging in many Linux and Unix-based systems, playing a crucial role in monitoring, debugging, and auditing. But not all syslog messages are created equal. Depending on your system, software, and logging configuration, syslog messages may follow different formats. This guide walks you through the different syslog formats, why they matter, and how to work with them effectively.

Every minute of system downtime costs enterprises a minimum of $5,000. With IT infrastructure growing more complex by the day, companies are put at risk of even greater losses. Adding insult to injury, traditional operations tools are woefully out of date. They can’t predict failures fast enough. They can’t scale with growing infrastructure.

Use case 1: When unexpected traffic surges lead to slower responses A sudden surge in user traffic during a high-demand event causes strain on resources in a cloud-based application running on App Engine. The platform automatically scales instances to handle the increased load, but since compute resources are shared, some instances experience CPU throttling. This leads to slower response times, delayed processing of critical operations, and potential errors that impact user experience. How to resolve it.

Time series data is particularly prevalent, seen across numerous different industries and use cases. It offers significant value to various organizations, highlighting the importance of effectively monitoring and analyzing the data. By analyzing and monitoring time series data you can understand trends, patterns, and anomalies in sequential data collected at many points in time.