Python data processing engine comparison with NYC Taxi trips (Bodo vs. Spark, Dask, Ray)

Python has become a popular choice for data engineers and data scientists, but scaling Python code efficiently remains a challenge. Compute engines like Bodo, Spark, Dask, and Ray/Modin aim to bridge this gap, offering Python scaling while striving for high performance. A recent benchmark tested the performance of these engines on a Python program that computes the summary of monthly trips with precipitation data on the NYC Taxi public dataset. The results reveal massive performance differences: Bodo delivered a 20x speedup over Spark (95% cost savings), 50x over Dask (98% cost savings), and a staggering 250x over Ray/Modin (99% cost savings). This is attributed to Bodo's HPC-based compiler approach, which differs from the distributed task scheduling design of other engines. The benchmark was conducted on a 4-node cluster setup on AWS, using a smaller subset of the dataset to allow local execution on a laptop. Bodo shows a roughly 4x improvement over Pandas, while other engines can be substantially slower than regular Pandas. The advantages of Bodo's architecture and design make it a strong competitor to existing engines like Spark, Dask, and Ray, offering unparalleled speed, ease-of-use, and cost efficiency for compute-heavy workloads.