To be production-ready often requires rewriting a significant portion of the native Python source code in another architecture. But what if you can scale linearly and reliably, in vanilla Python?
The issue with the Pandas apply function is that it can be unbearably slow when working with big data. Bodo makes up for Pandas’ lack of speed while staying equally powerful and user-friendly as Pandas.
The Monte Carlo approximation of Pi happens to be a popular example as well as one of the first examples demonstrating the Spark RDD API. Trying out Bodo on this benchmark on the occasion of Pi Day seemed suitable, and that is what this blog post is about.
According to Gartner, over 85% of data science projects fail. Bodo aims to solve this problem by eliminating key hurdles in the application development process.
Bodo is a novel analytics engine technology that empowers data scientists on all compute platforms (e.g. public or private cloud) to run Python workloads with the extreme performance and scalability of HPC without code rewrites.
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