The PyData ecosystem is home to a large number of vertically-scalable and easy-to-use solutions like Pandas, NumPy, and Scikit-Learn. The advent of horizontally-scalable cluster-solutions like Spark and Python’s powerful ability to act as “glue” between systems is giving rise to powerful new technologies in that connect the advanced analytics of PyData to the flexible scalability of Spark. We will discuss innovations in visualization, acceleration, and data-integration and how they can be used with Spark. PyData + Spark makes it easier than ever to create complete solutions that connect your in-house expertise with the deluge of data.
Peter has a B.A. in Physics from Cornell University, and has been developing commercial scientific computing and visualization software for over 15 years. His first product was a cutting-edge, photorealistic 3D graphics engine for engineering and architectural lighting simulation. He has built numerous proprietary Python-based data processing and visualization applications for clients in defense, medical imaging, engineering, and finance.
At Continuum, Peter primarily works on product engineering and software architecture for Wakari, Bokeh, and Blaze. As a creator of the PyData conference, he also devotes time and energy to growing the Python data community, and advocating and teaching Python at conferences worldwide.