Quasar Lite: Time-series Database for Edge Computing
- Do you work with time-series data (sensors, metrics...) on edge devices?
- Is ingestion speed a concern?
- Are you dissatisfied with the performance of SQLite?
- Does storage footprint matter? Would you benefit from a high compression ratio and on-the-fly downsampling?
- Does your application benefit from powerful, real-time analytics at the edge?
If you are interested in any of the questions above, here are the top 5 reasons why Quasar Lite is your ideal time-series database for edge computing :
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1. Industry-leading data compression ratio of 40:1
Thanks to industry-proven, patented compression algorithms, Quasar Lite compresses data faster than it arrives, significantly reducing disk usage while enabling fast queries. This is especially important for edge computing with a limited storage capacity.
2. Ingestion: millions of rows per second on edge devices
Achieved through streaming compression, novel data structures, and an innovative hybrid in-memory/disk architecture, Quasar Lite has a track record of handling large data sets even despite the constraints of an edge device.
3. No latency: real-time data analytics
In addition to data compression and fast ingestion capabilities, Quasar Lite also offers real-time, streaming data analytics, and an extremely optimized query engine for time-series data, delivering powerful, immediate analytics, right at the edge.
4. Small footprint
Quasar Lite is a lightweight, self-contained database engine that is well-suited for edge devices due to its efficiency, small footprint, and low resource requirements. It supports a wide range of architectures from ARM32 to Intel x64.
5. Quick deployment
Quasar Lite supports SQL queries, a native Python interface, and all the standards your business depends on, which makes it easier for industrial engineers and analysts to access and analyze data without requiring specialized programming skills.
