When to use `.parquet` and when to use `.csv`?

Following my article about parquet files, I thought I could give you some examples of when you might want to prefer .csv files and when you might want to use .parquet.

When could we use CSV files?

1. Storing small to medium-sized datasets that can be easily opened and read in spreadsheet programs like Microsoft Excel
2. Sharing data with others who may not have specialized software or knowledge of big data processing frameworks
3. Importing data into a database or other software application that requires a row-based format

When may you use Parquet files?

1. Storing and processing large datasets that require distributed processing frameworks like Apache Hadoop or Apache Spark
2. Performing complex queries and analyses on large datasets, such as machine learning or data mining tasks
3. Reducing storage requirements and improving query performance by compressing data

The conclusion might be similar to the one we have for the article Are .parquet files better than .csv files?

• CSV files are a good choice for small to medium-sized datasets that require a simple, row-based format that can be easily opened and read in a variety of software applications.
• Parquet files, on the other hand, are optimized for processing large datasets and can be more efficient for complex queries and analyses that require distributed processing frameworks.