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
When could we use CSV files?
- Storing small to medium-sized datasets that can be easily opened and read in spreadsheet programs like Microsoft Excel
- Sharing data with others who may not have specialized software or knowledge of big data processing frameworks
- Importing data into a database or other software application that requires a row-based format
When may you use Parquet files?
- Storing and processing large datasets that require distributed processing frameworks like Apache Hadoop or Apache Spark
- Performing complex queries and analyses on large datasets, such as machine learning or data mining tasks
- 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 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.