The SciDB Data Model
Multi-dimensional arrays beat tables
Geo-spatial data, scientific data, financial feeds, sensor data, sequencing data, time-series data, and other highly faceted data do not fit neatly or efficiently into tables, the data model used in relational databases.
SciDB’s native multi-dimensional array data model is designed from the ground up for ordered, highly dimensional, multi-faceted data. And data is never overwritten, allowing you to record and access data corrections and updates over time.
Dramatic storage and operational benefits
SciDB’s array data model provides dramatic storage efficiencies as the number of dimensions and attributes grows. Math operations run directly on the native data format. By partitioning data in N dimensions, not just 1 dimension, semantically related data can be efficiently accessed, speeding up clustering, array operations, and population selection.
Analyze More, Program LessA Webinar about Using SciDB for Computational Finance. Read more.
About Paradigm4Paradigm4 is the company behind the open source SciDB project. We develop it, support it, build enterprise extensions, and provide hands-on expertise.