Kubernetes Logs Monitoring & Observability

Kubernetes Logs Monitoring involves tracking and analyzing log data generated by applications and services running on Kubernetes clusters. It is crucial for identifying issues, troubleshooting, and optimizing performance in a dynamic containerized environment.

Atatus provides a comprehensive solution for Kubernetes Logs Monitoring, allowing users to aggregate, analyze, and visualize log data from their containerized applications. This helps in gaining insights into the performance and health of the applications.

Atatus allows you to monitor a variety of logs, including application logs, server logs, container logs, pod logs, Kubernetes system logs, network logs, security logs, custom logs, error logs, and performance logs.

Yes, Atatus allows you to set up custom alerts based on specific log events. You can define alert conditions, thresholds, and actions to be triggered when certain log events match your criteria. This proactive monitoring helps in identifying and addressing issues before they impact your users.

Atatus provides a centralized platform for log management, enabling you to search, filter, and analyze Kubernetes logs easily. With features like log aggregation, full-text search, and visualizations, troubleshooting becomes more efficient, allowing you to pinpoint issues and resolve them quickly.

Log data parsing prioritizes customer security by meticulously anonymizing Personally Identifiable Information (PII) data. Sensitive fields can be completely excluded during the parsing process, ensuring the privacy and compliance of customer information.

In Kubernetes Logs Monitoring, log pipelines are customizable workflows that allow users to filter, parse, and fine-tune log data based on specific criteria.

Log pipelines can be customized by using diverse filters and defining custom parsing rules. Users have the flexibility to configure pipelines to meet their specific monitoring requirements, ensuring that log data is processed and organized according to their preferences.

Yes, log pipelines can be configured to filter log data based on various attributes such as log source, severity, timestamp, or any custom field. This customization allows users to focus on specific aspects of log data that are most relevant to their monitoring goals.

Saved Views in Kubernetes Logs Monitoring enable you to create and save custom configurations for different troubleshooting scenarios. This feature simplifies collaboration, allows for quick context switching, and automates repetitive tasks, significantly improving overall efficiency.

If you exceed your log ingestion limits, we would contact you to discuss on stopping further processing new log data or upgrade your subscription.

You can choose to store logs in Atatus for a limited time (e.g., 7 days) or export them to external storage solutions like Amazon S3 for long-term retention.

To access historical log data beyond the retention period, you can rely on log data exports from Amazon S3, where you can push the logs into Atatus for further analysis.

Yes, Atatus provides users with the flexibility to customize log retention settings. Users can adjust retention periods based on their specific needs, aligning with compliance standards or internal data management policies.