Data engineers use Amazon S3 to store data in data lakes. A typical application is reading compressed Parquet files during the extract stage of an ETL pipeline.
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.
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.
A comparison of Python data processing engines using the NYC Taxi dataset evaluated performance and scalability. The results showed differences in speed and cost across data-intensive workloads.
Bodo gives teams a modern foundation for analytics in the AI era — where business users, data teams, and agents can work in the languages and tools they already know, with the trust and performance their enterprises require.
