Data Dictionary Generator & Metadata Analyzer
Upload or paste any structured dataset to automatically analyze metadata, inspect every column, detect physical and semantic data types, calculate value distributions, identify key candidates, and generate complete data dictionary documentation in Markdown, HTML, JSON, or Excel — 100% in-browser.
📋 Metadata & Data Dictionary Register
| # | Column Name | Friendly Label | Physical Type | Semantic Type | Nullability | Distinct / Unique | Metadata Attributes & Stats |
|---|---|---|---|---|---|---|---|
| Load a dataset and click Analyze & Generate Dictionary to see metadata register | |||||||
What is a Data Dictionary and Metadata Analyzer?
A data dictionary and metadata analyzer is a unified data inspection tool that extracts, profiles, and documents information about structured datasets. Metadata is "data about data" — detailing dataset-level properties, schema relationships, column definitions, physical data types, and semantic business meanings.
Without clear metadata analysis, team members spend substantial time guessing field meanings, deciphering technical column acronyms, or running custom SQL queries to figure out whether a column contains null values or unique identifiers. Combining metadata inspection with automatic data dictionary creation bridges technical storage schemas with business domain understanding.
By performing all profiling in your browser, this tool extracts both physical data types (Integer, Decimal, Date, Email) and business semantic categories (Customer ID, Order Total, Postal Code) to output clean, exportable documentation.
Why Metadata Analysis & Data Dictionaries Matter
Equip data analysts and engineers with clear column definitions and metadata summaries from day one.
Standardize semantic business types across departments to prevent conflicting metric calculations and reporting discrepancies.
Identify null risks, unexpected data types, and missing constraints before loading raw data into production databases.
Maintain a searchable inventory of sensitive fields (emails, phone numbers, customer identifiers) for privacy compliance.
Know exact column nullabilities, cardinalities, and key candidates before designing ETL pipelines or relational joins.
Analyze metadata and document internal datasets locally without uploading proprietary schemas to external servers.
Key Metadata & Dictionary Features
Designed for data architects, analytics engineers, and documentation maintainers.
Physical Type Detection
Distinguishes Integers, Decimals, Dates, Booleans, Emails, URLs, IP addresses, UUIDs, and Text strings automatically.
Semantic Business Inference
Heuristically identifies business concepts (Customer ID, Product SKU, Postal Code, Order Total) without guessing falsely.
Key Candidate Flagging
Detects 100% unique non-null columns as Primary Key candidates and flags high/low cardinality attributes.
Column Statistical Metrics
Calculates min, max, mean, median, standard deviation, null percentages, and sample value previews per field.
Markdown & HTML Exports
Generate ready-to-commit Markdown (`README.md`) dictionary tables or standalone HTML documentation pages.
Private In-Browser Execution
100% client-side JavaScript processing. Your data never leaves your device or touches external cloud APIs.
Supported Dataset Formats
Comma, tab, pipe, and semicolon delimited files with automatic separator detection.
Excel workbooks parsed seamlessly in-browser via SheetJS integration.
JSON object arrays and line-delimited JSONL/NDJSON datasets with nested object flattening.
Structured XML documents and YAML mapping sequences converted into tabular schema tables.
Document a dataset so other people can use it correctly
When a CSV or spreadsheet is handed to another team, column names alone rarely explain what the data means. A data dictionary helps people understand each field, its values, and potential quality issues before the dataset is imported, analysed, or maintained.
Load the supported dataset and confirm that the first row is interpreted correctly as column headings when appropriate.
Review each column's name, detected value patterns, missing values, and other available statistics. Treat automated descriptions as suggestions to verify.
Export the dictionary in the available format, then add business definitions, units, ownership, and rules that cannot be inferred from the data alone.
Use the generated dictionary as a starting point
A column named `status` might mean an order state, an account state, or something else entirely. Statistics can show that a column contains repeated labels or missing values, but they cannot reliably explain the business meaning without context. Check field names and sample values with the person or system that owns the data.
Before sharing a dictionary, remove sensitive sample values if they are not needed. Review inferred data types and completeness metrics against the real dataset, and keep a copy of the source so later updates can be compared.
Frequently Asked Questions
What is a data dictionary and how does this tool work as a metadata analyzer?
A data dictionary is a metadata reference that details column names, data types, business meanings, nullability constraints, and value distributions for a dataset. This tool serves as a metadata analyzer by automatically inspecting schemas and profiling columns in your browser.
How does automatic data type and semantic type detection work?
The generator analyzes row values alongside header names using regex pattern matching, statistical distributions, and heuristics to infer physical types (Integer, Decimal, Date, Email) and business semantic categories (Customer ID, Total, Postal Code).
Is my dataset uploaded to any external server during metadata analysis?
No. All profiling, metadata extraction, and documentation generation run 100% locally inside your web browser session.
What export formats are available for the data dictionary and metadata reports?
You can export data dictionaries and metadata reports in Markdown (.md), HTML Report, JSON metadata schema, Excel (.xlsx), or CSV format.
Analyze Metadata & Document Your Dataset Now
Upload your data file and generate complete, exportable metadata analysis and data dictionary documentation in seconds — with total privacy.