Data analysis is an exciting journey, but it comes with its challenges. One of the biggest hurdles is identifying anomalies—unexpected results that can distort our conclusions and predictions.

As I had indicated in Part I, I had built some basic examples with PyIceberg and Python to learn more, and exercise some of the functionality it offers. I started by using data that I collect from time-to-time, for securities, mostly common stocks, and various twelve-month key metrics and analyst forecasts. This is an extension to my SEC filings collection that I have a running series of articles on. I use this particular data to build out details for securities in my Neo4j g

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.