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.
