Python has proven to be a popular choice for data scientists in the domain of graph analytics. The multitude of freely available frameworks and python packages allow to develop applications quickly through ease of expressibility and reuse of code. With petabytes of data generated everyday and an ever evolving landscape of hardware solutions, we observe a graph processing framework should expose the following characteristics: ease of use, scalability, interoperability across data formats, and portability across hardware vendors. While existing python packages have been helping to drive application development, our assessment is that none of the packages address all the aforementioned challenges. We propose a community led, open source effort, to design and build a graph processing python library to specifically address these challenges.
Speakers: Vincent Cave