Platform Overview
Platform Overview

An Agile Science Platform Designed for Scientific Data and Scientific Computing by a Turing Laureate

With science-ready data harmonized in a scalable scientific computational platform, SciDB & REVEAL expedite integrative, collaborative, and reproducible research.

SciDB is an entirely new scientific data management and computational platform organized around vectors and multidimensional arrays as the basic data modeling, storage, and computational unit.

It’s an all-in-one storage and elastic computing platform— a massively parallel, transaction-safe, array-oriented, analytics platform.

Heterogeneous data types—including single cell data, ‘omics data, imaging data, instrument data, wearables, environmental data and more—are naturally represented in an array data model.


platform illustration

RAPID & FAIR data access from the ground up.

SciDB rapidly serves up the selected data of interest because it preserves the logical structure and co-locality of data in its native data storage format. It’s what makes SciDB a scientific data management system at its core and differentiates it.

SciDB runs analytics, signal processing, image processing, and machine-learning elastically in parallel on data distributed across a cluster…on any cloud or on-premises.

With Burst-Mode, the elastic cluster automatically expands to thousands of CPU cores to handle large compute tasks, while leveraging cheaply-available “spot” capacity – maximizing calculation speed and minimizing total compute cost.

Familiar interfaces for scientific analysis

Data scientists and bioinformatics researchers work in R and Python, so we made R and Python the query languages for SciDB. SciDB brings the computation to the data, running math and machine-learning functions from any library on the data in its native data format. Selected data and/or the results of large computations can be exported to R or Python for further analysis and visualization. Workflows are captured and documented in R markdown and Jupyter notebooks. REST APIs provide an interoperable, HTTP interface.

Apps, not Ecosystems

Scientists also told us they wanted higher-level, use-case focused solutions, not just workspaces which require them to assemble their own

Multiple users can load, read, and write data in a secure, transactionally safe manner as data operations are guaranteed to be atomic and consistent (ACID compliant).

Together SciDB and REVEAL™ provide an AGILE SCIENCE™ platform for expediting translational research.

For a deeper technology dive, contact us.

“The greatest advantage that truly sets SciDB apart from other Big Data analysis technologies, I believe, is the optimized communication protocol that Paradigm4 implemented to support efficient multimodal parallelization; that is, beyond embarrassingly parallel processing. This protocol empowers SciDB users —  who are normally unaware of the existence of the protocol — to leverage its functions and operators with exceptional performance for a wider range of analyses on arrays.”

Kwo-Sen Kuo, Ph.D., Bayesics.