Distributed processing can be used to implement algorithms capable of handling massive datasets by distributing smaller tasks across a cluster of computers. Over the years, many software packages, such as Apache Hadoop, have been developed to implement performant and reliable execution of distributed software.

In this chapter, we learned about the architecture and usage of Python packages, such as Dask and PySpark, which provide powerful APIs to design and execute programs capable of scaling to hundreds of machines. We also briefly looked at MPI, a library that has been used for decades to distribute work on supercomputers designed for academic research.

Throughout this book, we explored several techniques to improve the performance of our program, and to increase the speed of our programs and the size of the datasets we are able to process. In the next chapter, we will describe the strategies and best practices to write and maintain high-performance code.