More and more frequently, owners of Hadoop deployments find themselves facing the challenge of supporting data science ecosystems like Python and R, both adjacent to and within their Hadoop infrastructure. Although these technologies promise powerful data science insights, they can also be complex to manage and deploy. As people build out data science sandboxes and production environments, they discover a number of challenges ranging from basic package management and data lineage to reproducibility and governance of data science artifacts.
Peter Wang distills the vast array of Hadoop and data science tools and architectures down to the essentials that deliver a powerful and lightweight stack quickly so that you can accelerate time to value while meeting your data science, governance, and IT needs. Throughout the discussion, Peter highlights challenges and best practices drawn from real-world customer use cases.
This session is sponsored by Continuum Analytics.
Peter Wang is the cofounder and CTO of Anaconda, where he leads the product engineering team for the Anaconda platform and open source projects including Bokeh and Blaze. Peter’s been developing commercial scientific computing and visualization software for over 15 years and has software design and development experience across a broad variety of areas, including 3-D graphics, geophysics, financial risk modeling, large data simulation and visualization, and medical imaging. As a creator of the PyData conference, he also devotes time and energy to growing the Python data community by advocating, teaching, and speaking about Python at conferences worldwide. Peter holds a BA in physics from Cornell University.
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