The two previous articles where concerning evaluation of Apache Iceberg and it's capabilities, as well as using PyIceberg to create objects and load data. This post will focus on getting data out, and using Pandas dataframes to create analytics.

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

Choosing between HDF5 and Apache Iceberg can feel overwhelming due to their distinct features and advantages. Armed with the right knowledge, you are better equipped to make a decision tailored to your data science needs.

In today's fast-paced digital world, data is a powerful asset for organizations. With the increasing volume of data, companies need innovative solutions to handle this wealth of information efficiently. One such breakthrough technology is Apache Iceberg.