What is CSV

CSV (Comma-Separated Values) is a simple, plain-text data format where each line represents a row and each column is separated (delimited) by a comma (or sometimes another delimiter like a semicolon).

How It Works

A CSV file stores tabular data in lines of text. Each line corresponds to one row, with commas (or a chosen delimiter) marking the boundaries between columns. Many tools, including spreadsheet software and databases, can import or export CSV files. You can add a header row to label columns, and the rest of the rows contain the actual data.

Technical Details

By default, CSV uses commas as delimiters, but you can specify other separators (e.g., tabs, semicolons). Text fields containing commas or line breaks are often wrapped in double quotes. While CSV is easy to read and widely compatible, it lacks a strict schema definition and can have varied interpretations, especially around how to handle special characters or nested data.

How to Write It

Basic Syntax

-- Basic SQL-based examples for importing/exporting CSV (PostgreSQL style):

-- 1. Import a CSV file into a table
COPY users(id, name, email)
FROM 'path/to/file.csv'
WITH (FORMAT csv, HEADER true);

-- 2. Export a table to a CSV file
COPY users
TO 'path/to/export.csv'
WITH (FORMAT csv, HEADER true);

Learn More

Best Practices

Include a header row for clarity and easier importing.
Wrap fields containing commas or newlines in quotes.
Use consistent character encodings (UTF-8) to avoid unreadable characters.
Choose sensible column separators (commas, semicolons, or tabs), depending on regional settings.

Common Pitfalls

Mismatched quotes or extra delimiters can corrupt rows.
Inconsistent use of headers across multiple files leads to confusion.
Large CSV files can be slow to process line-by-line or import at scale.
Lack of data types can make it hard to validate data before importing.

Advanced Tips

Use chunking or streaming when handling extremely large CSV files to avoid memory issues.
Employ CSV-specific libraries (e.g., OpenCSV in Java, csv in Python) for robust parsing.
Transform CSV data into structured formats (like JSON or Avro) for more consistent schemas.
Keep automated logs of CSV imports to track any failed or partial loads.