Struggling to choose between Axibase Time Series Database and Ganglia? Both products offer unique advantages, making it a tough decision.
Axibase Time Series Database is a Ai Tools & Services solution with tags like time-series, database, iot, devops, it-monitoring.
It boasts features such as Store and query numeric time series data, Analyze time series data using SQL queries, Visualize time series data using built-in graphing tools, Real-time aggregation and filtering of time series data, Rule-based alerting on time series, Plugin architecture to extend functionality, REST API for integration and pros including Purpose-built for time series data, High performance and scalability, Powerful analytics capabilities, Open source and free to use, Easy to set up and use.
On the other hand, Ganglia is a Network & Admin product tagged with monitoring, metrics, utilization, bottlenecks, faults, distributed-systems.
Its standout features include Real-time monitoring of clusters and grids, Collection of metrics like CPU usage, memory usage, network traffic, Visualization of metrics through web interface, Alerting based on thresholds, Support for heterogeneous clusters with different architectures, Scalable to clusters with thousands of nodes, and it shines with pros like Open source and free, Easy to set up and configure, Low overhead, Web interface for easy access to metrics, Extensible and customizable using plugins, Widely used and supported.
To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.
Axibase Time Series Database (ATSD) is an open-source time series database optimized for collecting, storing, analyzing, graphing, and visualizing numeric time series data. It is designed for IoT/DevOps/IT monitoring use cases.
Ganglia is an open-source monitoring system for high-performance computing systems such as clusters and grids. It collects and visualizes various metrics like CPU utilization, memory usage, network traffic etc. in real-time. It allows for easy identification of bottlenecks and faults in distributed systems.