Today I’m thrilled to announce our partnership with Snowflake, accompanied by a strategic investment they have made in Bodo.
Previously, I’ve benchmarked Bodo using the popular example: The Monte Carlo approximation of Pi. In this post, I wanted to test how Bodo performs on another popular data analytics example benchmark: Word Count of Beer Review.
Findings derived from the standard TPC-H benchmarks (22 in all) to compare Bodo’s economic and speed performance to Spark, Dask, and Ray for data processing workloads.
Earlier this month, ZDNet reported that Python is challenging C and Java to become the most popular programming language. We’d like to see that happen. But maybe not why you think.
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.
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.
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