8 Cambridge Center, Cambridge, MA
Complex analytics should work as nimbly on extremely large data sets as on small ones. You don't want to think about whether your data fits in-memory, about parallelism, or formatting data for math packages. You'd like to use your favorite analytical language and have it transparently scale up to Big Data volumes.
Learn about SciDB—the massively scalable, open source, array database with native complex analytics, integrated with R, Python, and Java. See how SciDB enables you to:
• Explore rich datasets interactively
• Do complex math in-database—without being constrained by memory limitations
• Perform multi-dimensional windowing, filtering, and aggregation
• Offload large computations to a commodity hardware cluster—on-premise or in a cloud
• Share data among users, with multi-user data integrity guarantees and version control
Presented by: Alex Poliakov, Paradigm4
Alex has over a decade of experience in developing distributed database internals. As Solutions Architect for Paradigm 4, he helps data scientists grapple with big analytics in multiple industries from e-commerce to genomics, while also contributing to the design and development of the core SciDB engine.