Feature
Geospatial data (R-tree)
RaimaDB supports an index algorithm designed specifically for geospatial data called an R-Tree. It is a balanced search tree that organizes its data into pages and is designed to group physically nearby objects and then represent that in the next level of the tree. This is the ideal index type when the users need quick retrieval of multi-dimensional data within a bounding box. A common use case for an R-tree might be to store spatial objects such as restaurant locations or the polygons that typical maps are made of: streets, buildings, outlines of lakes, coastlines, etc.
Aerospace
Automotive
Industrial
Energy
Aerospace
Aerospace and defense
Autonomous flight
Live data capture
Post flight analysis
Flight planning
Capture lots of data Reliably store data Process data in real-time Replicate data to the cloud Securely communicate data Low compute High data volumes Backlogged data not lost
Automotive
Automotive industry
Autonomous driving
Historical driving information
Infotainment & UX
Capture map data Reliably store data Process data in real-time Use irregular information Store alerted information Merge sensor and map data Triggers / Actionable
Industrial
Industrial automation
Live data capture
Smarter machines
Capture sensor data Reliably store data Process data in real-time Talk to other devices Light footprint Low compute Triggers / Actionable Backlogged data not lost
Energy
Energy & natural resources
Live data capture
Smarter machines
Capture sensor data Reliably store data Process data in real-time Use irregular information Store alerted information Low compute Light footprint Triggers / Actionable Get started with RaimaDB today
Try RaimaDB for free today and see how screaming fast data management can get you to market on schedule and under budget.
