Felix Fernandez works since 01.02.2013 as Business CIO for the Cash & Derivatives IT department within Deutsche Börse IT. His team of approx. 500 people is responsible for the development and maintenance of all major systems for trading and clearing of cash and derivatives instruments of Deutsche Börse Group. Felix has a background in information technology with a diploma from the University of Applied Sciences in Frankfurt, Germany. He works for more than 20 years in the financial industry and has an extensive experience in the exchange business. His activities in the last years have been focused around quantitative modeling and simulation of exchange pricing and market analysis, leading a team of mathematicians, software engineers and financial engineers before he moved back to the IT.
In the context of a rapidly evolving financial industry, managing increasing amounts of data and coping with regulatory requirements, time-to market of services and cost efficiency along the value chain are key success drivers for any financial institution. Especially the shift from monolithic architectures (e.g. Open VMS/Cobol) to heterogeneous technology stacks and systems (e.g. Linux/Java/SQL) creates additional challenges for IT. In addition, the “technology empowerment of the business analysts” adds complexity to the implementation of IT systems if not managed properly. After the introduction of Python at Deutsche Börse Group several years ago, the presentation today is a reflection about experiences in real world applications, the potential of Python as a universal tool for end-to-end development and an outlook to the future of this language framework in the financial industry.
Gaël Varoquaux is an INRIA faculty researcher working on computational science for brain imaging in the Neurospin brain research institute (Paris, France). His research focuses on modeling and mining brain activity in relation to cognition. Years before the NSA, he was hoping to make bleeding-edge data processing available across new fields, and he has been working on a mastermind plan building easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, and Mayavi, a nominated member of the PSF, and often teaches scientific computing with Python using http://scipy-lectures.github.com. His infrequent thoughts can be found at http://gael-varoquaux.info
As a penniless academic I wanted to do "big data" for science. Open source, Python, and simple patterns were the way forward. Staying on top of todays growing datasets is an arm race. Data analytics machinery —clusters, NOSQL, visualization, Hadoop, machine learning, ...— can spread a team's resources thin. Focusing on simple patterns, lightweight technologies, and a good understanding of the applications gets us most of the way for a fraction of the cost.
I will present a personal perspective on ten years of scientific data processing with Python. What are the emerging patterns in data processing? How can modern data-mining ideas be used without a big engineering team? What constraints and design trade-offs govern software projects like scikit-learn, Mayavi, or joblib? How can we make the most out of distributed hardware with simple framework-less code?