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CSV and Parquet: A Data Format Comparison

This episode explores **CSV (Comma-Separated Values) files**, a common plain text format for tabular data. *What is CSV?* A plain text file where values are separated by commas and rows by line breaks. It's *human-readable* and universally viewable in text editors and spreadsheet programs. *Key Uses:* Widely used for *data import/export* between software, data analysis, migration, backup, reporting, and machine learning datasets. *Benefits:* Offers *broad compatibility* across applications and languages, is *efficient* due to its lightweight structure, and is simple to create, read, and manually edit. *Limitations:* Suffers from a *lack of standardization**, leading to inconsistent formatting and user errors like missing data or encoding issues. It also has **security concerns* like CSV Injection and lacks built-in data validation or encryption. For large datasets, CSVs are *inefficient* due to their row-based structure and size limits in programs like Excel. They also lack schema definition, making data type inference challenging. *CSV vs. Parquet:* *CSV:* Simple, human-readable, best for *small datasets* or quick manual analysis. *Parquet:* A *columnar binary format* designed for *large datasets**. It offers significantly **better compression**, **faster query performance* (by only reading relevant columns), and **embeds schema/data types**, ensuring data integrity and efficiency for analytical workloads. Parquet is not human-readable.