Compare to Relational Databases
When it comes to databases, one size doesn’t fit all.
SciDB is a computational database – not a data warehouse, not a business-intelligence database, and not a transactional database.
Conventional databases have the wrong data model for complex analytics. Machine-generated data, scientific data, and data with spatial or temporal ordering do not fit neatly into a relational table, leading to inefficient storage and poor analytics performance.
And conventional databases are missing critical functionality like data versioning that support reproducing and auditing results.
SciDB’s multi-dimensional array data model is a natural match for complex data like genetic sequencing data, sensor data, financial data and geospatial data. And since SciDB is an all-in-one database and analytics platform, you don’t have to extract, reformat, and export your data to a standalone math package.
SciDB is designed from the ground up for large-scale complex analytics:
- Provides automated, transparent support for massively distributed data sets.
- Accelerates array operations by 10-100x because data is stored natively in an array format.
- Natively integrates complex analytics directly with data management, supporting ad-hoc analytics at scale.
- Supports reproducing and auditing of results because data is never overwritten, only updated.
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.