A CSV (Comma Separated Values) file is a plain text file that stores tabular data (rows and columns) in a simple format, where each line represents a data record and commas separate values.
A CSV file stores data in a table-like structure, where:
• Each line = a row
• Each value = a column
• Values are separated by a delimiter (usually a comma ,)
Example:
name,age,city
Alice,25,London
Bob,30,Istanbul
Why we use CSV?
• To store structured data in a simple text format
• To exchange data between systems (databases, spreadsheets, apps)
• To import/export data in tools like Excel, databases, analytics tools
• To handle lightweight datasets without complex formats
When should you use CSV?
CSV is a good fit when:
• You need simple data storage
• You are moving data between systems (ETL jobs)
• You work with spreadsheets or tabular data
• You want a human-readable format
• You don’t need complex data types or relationships
Not ideal when:
• You need nested or hierarchical data (JSON is better)
• You require schema enforcement or validation
• You deal with large-scale analytics systems needing optimized formats
• You need binary efficiency (Parquet/ORC are better)
Key features of CSV File Format
• Plain text format
• Human-readable
• Lightweight and simple
• Supports basic tabular structure
• Can be opened in Excel, Google Sheets, etc.
• Flexible delimiter (comma, semicolon, tab)
Structure rules of CSV
• First row often contains headers
• Each row must have same number of columns
• Values can be:
- Numbers
- Strings
- Dates (as text)
Example with quoted values:
name,comment
Alice,"Hello, world"
Bob,"CSV supports commas inside quotes"
Advantages
• Very simple and universal
• Supported by almost every tool and language
• Easy to generate and read
• Lightweight (no overhead)
• Great for data exchange
Disadvantages
• No strict data types
• No built-in schema validation
• Poor support for complex/nested data
• Can be error-prone (escaping, commas, quotes)
• Not efficient for large-scale analytics storage
Alternatives
JSON
Better for nested and structured data
XML
Verbose but highly structured
Parquet
Columnar format optimized for big data (used in systems like Apache Spark)
ORC
Optimized for Hadoop ecosystem (used with Apache Hive)
Avro
Compact binary format with schema support (used in Apache Kafka pipelines)
