The latest news, insights, product release information, guides, and everything in between from the Bodo team.
For a long time, the python multiprocessing library has been a solution for many data scientists and engineers to get faster results when processing time is a pain point. I want to show you a much faster solution: Bodo.
Today I’m happy to announce our collaboration with Xilinx, Inc., including their taking an investment in Bodo. This will be meaningful to more than just Xilinx and Bodo customers... It signifies another stage of our democratizing access to large-scale parallel computing using Python.
What if we could improve analytics performance by 1,000x and and reduce aggregate operational expenses costs to 1/10 -- using the programming techniques and hardware already in use today? With our Series A funding, that’s what we at Bodo are committed to doing.
A large Fortune 10 enterprise evaluated Bodo for data engineering workloads in their new data infrastructure. They found that Bodo is much simpler to use and 10x faster than highly optimized Spark with the same cluster setup.
Using only a single core for computation on a dataset with millions or even billions of rows can quickly become a disaster. The good news is Bodo has a solution for data scientists when Pandas does not meet their expectations.
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.