OpenNMS vs Zabbix
July 25, 2023 | Author: Michael Stromann
See also:
Top 10 IT Monitoring software
Top 10 IT Monitoring software
OpenNMS and Zabbix are both popular open-source network monitoring and management platforms, each offering distinct features and capabilities. OpenNMS is known for its scalability and flexibility, making it ideal for large and complex network environments. It provides automatic discovery of network devices, event and performance management, and advanced reporting. OpenNMS also offers extensive customization options, allowing users to tailor the platform to their specific monitoring needs. On the other hand, Zabbix is also a robust network monitoring tool, focusing on real-time monitoring and alerting. It offers a user-friendly interface and supports a wide range of monitoring methods, including SNMP, IPMI, and JMX. Zabbix excels in providing detailed performance metrics and trend analysis, allowing users to identify network issues and bottlenecks quickly.
See also: Top 10 IT Monitoring software
See also: Top 10 IT Monitoring software
OpenNMS vs Zabbix in our news:
2019. Zabbix 4.2 adds built-in support of Prometheus data collection
Zabbix Team has recently unveiled the launch of Zabbix 4.2. This latest version introduces a comprehensive monitoring system equipped with cutting-edge features, including data collection and processing, distributed monitoring, real-time problem and anomaly detection, alerting and escalations, visualization, and more. Zabbix 4.2 significantly enhances data collection capabilities, supporting diverse methods such as push and pull from various sources like JMX, SNMP, WMI, HTTP/HTTPS, RestAPI, XML Soap, SSH, Telnet, agents, scripts, and more. Notably, the integration with Prometheus has been added as a new feature, allowing native support for the PromQL language. Additionally, the utilization of dependent metrics empowers the Zabbix team to efficiently gather a vast amount of Prometheus metrics. By making a single HTTP call, all the required data can be retrieved and subsequently reused for relevant dependent metrics.