CSV Dictionary: Data Fields and Formatting Specifications Comma-Separated Values (CSV) files are the backbone of data exchange. They are simple, text-based, and universally compatible. However, without strict formatting rules, CSV files quickly break down during import processes.
A CSV Data Dictionary prevents these errors. It serves as the single source of truth for developers, data analysts, and automated systems, defining exactly how data must be structured, typed, and formatted before transmission. 1. Core Structural Specifications
Every CSV file must adhere to standard structural constraints to ensure predictable parsing across different programming languages and database engines.
Character Encoding: UTF-8 strictly. This prevents corruption of special characters, diacritics, and non-English alphabets.
Row Delimiter: Standard Unix line feeds (
).
Column Delimiter: A standard comma ,. If a data field contains a comma, the entire field must be wrapped in double quotes (e.g., “Los Angeles, CA”).
Text Qualification: Double quotes “ are used to enclose fields containing delimiters, line breaks, or leading/trailing whitespaces. If a literal double quote exists inside the data, it must be escaped by doubling it (e.g., “The manager said, ““Approved”” “ ).
Header Row: The first row of the CSV file must contain the exact column names specified in the data dictionary. Header names are case-sensitive and must not contain spaces. 2. Global Data Field Specifications
The following matrix defines the standard technical constraints for the most common data types used in enterprise CSV exchanges. Field Type Formatting Requirement Example Value Validation Rule String / Text Alphanumeric, UTF-8, stripped of leading/trailing spaces. John Doe Max length constraints apply per field. Integer Whole numbers only. No commas or periods. 15420 Digits only. Optional leading negative sign -. Decimal / Float Period . as decimal separator. No thousands separators. 1250.45 Match specific precision (e.g., 2 decimal places). Boolean Standardized uppercase text tokens. TRUE / FALSE Binary choices only. Reject ⁄0, Y/N, or Yes/No. Date ISO 8601 standard format. 2026-06-03 YYYY-MM-DD Timestamp ISO 8601 with UTC offset. 2026-06-03T11:22:00Z YYYY-MM-DDTHH:MM:SSZ 3. Handling Special Data Conditions
System integrations often fail because edge cases are left undefined. Establish clear rules for the following scenarios: Null and Missing Values
Representing Empty Fields: Leave the field entirely empty between delimiters (e.g., value1,,value3).
Avoid Filler Text: Do not use string literals like NULL, NaN, N/A, or spaces to represent missing data, as parsers will read them as literal text strings. Leading Zeros
Data Integrity: Numeric strings that rely on leading zeros (like US Zip Codes or internal SKU numbers) must be treated as Strings, not Integers.
Prevention: Standard CSV parsers often strip leading zeros (turning 00123 into 123). Enclosing the value in double quotes (“00123”) signals to the importing system to preserve the character length. 4. File Validation Workflow
To maintain high data quality, implement a automated three-step validation pipeline before consuming any CSV file:
Structural Validation: Verify the file matches UTF-8 encoding, utilizes the correct delimiters, and has the exact number of columns required by the header row.
Schema Validation: Check that every column aligns with its declared data type (e.g., ensuring a date column does not contain text).
Business Logic Validation: Ensure numbers fall within realistic ranges, required fields are not empty, and unique identifiers (like IDs) are not duplicated. To help tailor this document for your system, let me know:
What specific system or database (e.g., PostgreSQL, Salesforce, Excel) will consume this CSV?
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