Distributed data processing
For truly massive datasets, distributed processing becomes necessary. Here’s an example using Dask, a flexible library for parallel computing in Python (https://www.dask.org/).
Dask and Apache Spark are both distributed computing frameworks, but their main differences lie in their architecture and use cases. Spark is built around the concept of resilient distributed datasets (RDDs) and requires a cluster setup, making it ideal for large-scale production data processing. Dask, on the other hand, is designed to integrate seamlessly with the Python ecosystem and can scale from a single laptop to a cluster, using familiar APIs that mirror NumPy, pandas, and scikit-learn. While Spark excels at batch processing of massive datasets, Dask is more flexible for interactive computing and scientific workflows, particularly when working with Python-native libraries and when you need to scale up existing Python code with minimal modifications.
Let’...