Apache Spark is a powerful open-source distributed computing system that excels in big data processing. It is lauded for its speed and ease of use, making it a favorite among software engineers and data scientists.

Data processing and statistics gathering are essential tasks in today's data-driven world. Engineers frequently find themselves choosing between tools like PySpark and Scala when embarking on these tasks.

Dask is a flexible library for parallel computing in Python. It is designed to scale from a single machine to a cluster of machines seamlessly. By using Dask, you can manage and manipulate large datasets that are too big to fit into memory on a single machine.

In today's data-driven world, organizations are generating vast amounts of data every second. This data can be a goldmine for insights when processed and analyzed effectively. One of the most powerful tools in this realm is Apache Spark.

Several years ago, I started a side project that I thought would be fun: collecting and loading SEC filings for ETF and Mutual Fund Holdings on a monthly basis. I wanted to essentially automate the collection of the SEC filings

The processing design utilizes batches alongside the multi-processing (mp) Python module to handle processes and resources. Each content "classification" is encapsulated in its own class, such as ImageProcessor, VideoProcessor, TextFileProcessor, etc.

https://www.kdnuggets.com/introduction-dask-python-data-scientist-power-tool

https://medium.com/towards-data-engineering/data-engineering-2-0-trends-that-are-shaping-the-industrys-future-8d9415ddaa1d