- Good Stuff
Data is dynamic. New data is continuously being captured, and older data may be updated to correct bad data points, fill in missing values, or substitute context-sensitive default values. Both scientific and commercial applications may need to re-analyze data, to reproduce results, compare results, validate results, and support audits.
SciDB contains capabilities for versioning your data and for distinguishing among many interpretations of missing values.
|Data is never overwritten, even when it is updated. SciDB versions data so you can re-analyze previous moment-in-time datasets.|
|SciDB query languages let you refer to specific array versions within queries, allowing you to reproduce previous results at any time.|
Missing Data Reason Codes
|SciDB supports multiple, custom-defined null values (such as data missing, source unavailable, trading halted, or instrument error) so that applications can substitute context-sensitive values.|
Python Users! Get Big Analytics without Big Hassles:A Webinar for SciDB-Python Users. Read more.
Request SciDBContact us to download your copy of SciDB 13.11. 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.