(Recorded on July 16, 2014)

Sponsored by
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The productivity of your lab depends on your ability to do rapid, exploratory and reproducible analytics on extremely large data sets comprising diverse data types and sources both public and private. But informatics researchers get slowed down by traditional analytical tools: forced to think about finding data in thousands of files, about whether their data fits in memory, about formatting and transporting data into a math package, and getting results fast enough.

There’s another way, one that lets analysts readily share, access, and analyze unlimited heterogeneous data as well as use their favorite analytical languages and have it transparently scale up to Big Data volumes. Paradigm4 presents a webinar about SciDB—the massively scalable, open source, computational database with native complex analytics, integrated with R and Python—for translational informatics research.

Learning Objectives:

Learn how SciDB lets you:

  • Rapidly join, explore, and analyze public data like TCGA with proprietary data
  • Do complex math in-database—seamlessly offloading large computations to a grid or cloud
  • Use R and Python to manage and analyze massive SciDB arrays as if they were R or Python objects
  • Share data among users, with multi-user data integrity guarantees and version control


Bryan Lewis, Ph.D. Applied Math,Chief Data Scientist, Paradigm4

Alex Poliakov, Solutions Architect, Paradigm4


Marilyn Matz, CEO & Co-founder, Paradigm4