From CSV to the Parquet file format

The traditional approach of storing structured data in CSV files has long been the method of choice for many organizations. For example, the very dataset used in Chapter 9, Loading Large Datasets beyond the Available RAM in Power BI, which contains monthly U.S. flight data from 1987 to 2012, consists of many CSV files. However, this approach has several significant limitations that can negatively impact data processing and analysis:

1. The CSV file format is not optimized for columnar storage and stores data in a row-based format. As a result, CSV files can have slower read and write times, especially for large datasets. This can result in slower query execution times and reduced overall performance, negatively impacting the efficiency of data processing and analysis.
2. Although CSV files can handle basic data types such as integers and strings, they can struggle when it comes to dealing with more complex data structures such as arrays and nested data types...