Struggling to choose between Axibase Time Series Database and OpenTSDB? 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, OpenTSDB is a Development product tagged with time-series, monitoring, analytics.
Its standout features include Distributed and horizontally scalable architecture, Built on top of HBase for reliability and scalability, Customizable rollup tables for aggregating data, Tag-based metric model for organizing time series data, HTTP API for writing and querying data, Support for downsampling and aggregation of data, Plugin architecture for adding functionality, and it shines with pros like Handles massive amounts of time series data, Low latency queries, Easy to scale horizontally, Integrates well with Hadoop ecosystem, Open source and free to use.
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.
OpenTSDB is a distributed, scalable time series database optimized for storing and serving massive amounts of time series data without losing granularity. It's designed to be used as a backend for monitoring systems and analytics platforms.