The World is Thirsty for Dramatic Improvements in Price/Performance for Geospatial
Geospatial data is one of the fastest growing domains for both data collection and analytics due to the prevalence of sensors and IoT devices. A wealth of valuable location-based data is geing generated. To unlock its value, an exponential improvement is needed for organizing geospatial data at massive scale.
Geospatial and spatial/temporal data is multi-dimensional in nature. Traditional data management algorithms can't quickly organize and search multi-dimensional data at large scale. Craxel's O(1) breakthrough provides unprecedented speed of ingest and speed of query for multi-dimensional data.
Creating a Timeline of Everything at a Location
Black Forest time series graphs enable a timeline to be built of all information about a location. The location can be a point location or an area. Many different record types can be stored in this timeline about this location and quickly accessed in a single query. Black Forest makes it affordable to keep massive quantities of data for long periods of time about these locations.
Black Forest provides the ability to connect the dots because all the relationships between the locations and the people, places, and things that intersect with them can also be stored in the time series graph. Black Forest can do this at unimaginable scale because of its O(1) multi-dimensional indexing breakthrough.
Rapid Time to Insight for Geospatial Data Increases Analyst and Algorithm Productivity
Black Forest is uniquely designed to handle data volume and data normalization to support rapid ingest and analysis of geospatial data at petabyte scale. Our ingest pipeline framework provides a flexible and scalable architecture to transform disparate data sets and ingest data based on your timeliness requirements. Data is stored in efficient, hyperscale storage. Black Forest delivers fast query responses directly from hyperscale storage. This makes it affordable to keep as much historical data as you want.
With Craxel, the way insight is extracted from data is fundamentally different:
- Data is organized as time series graphs
- Records are indexed immediately as they are ingested and readily available for use
- Records of different types are co-located in the same time series graph
- Time to insight is pivotally reduced for ALL types of data
