Profile & Analyze JSON Data
online, free & instant
Paste any JSON and instantly see type distributions, null percentages, min/max/mean values, unique counts, and string lengths. Runs entirely in your browser — your data stays private.
Open JSON Data ProfilerUnderstand your JSON data in seconds
Privacy-first data profiling
100% Client-Side
Your JSON never leaves your device. All profiling runs in the browser — no server, no upload, no risk.
Instant Results
Statistics are computed as you paste. No waiting for a backend — results appear in milliseconds.
Comprehensive Stats
Type distribution, null rates, numeric ranges, unique counts, and string lengths — all in one view.
Related JSON tools
Common questions answered
What does a JSON data profiler do?
A JSON data profiler analyzes the structure and content of your JSON data. It reports type distribution for each field (string, number, boolean, null, object, array), calculates null percentages, computes min/max/mean for numeric fields, counts unique values, and measures string lengths — giving you a complete picture of your data quality.
How can I check data quality in a JSON API response?
Paste your API response into the profiler to instantly see which fields contain nulls, how consistent your data types are, and whether numeric ranges make sense. This helps catch missing data, type mismatches, and unexpected values before they reach production.
What type analysis does the profiler provide?
The profiler breaks down each field by JSON type: string, number, boolean, null, object, and array. It shows the percentage of each type per field, making it easy to spot mixed-type columns or unexpected nulls in your dataset.
Can I profile large JSON files?
Yes. Because all processing happens in your browser using JavaScript, there is no upload size limit imposed by a server. Performance depends on your device, but typical datasets up to tens of megabytes are handled smoothly.
Is my JSON data safe when using this profiler?
Absolutely. The profiler runs 100% client-side in your browser. Your data is never sent to any server, never stored, and never logged. You can even disconnect from the internet and it will still work.
What is JSON data profiling?
JSON data profiling analyzes a dataset and produces summary statistics about its structure, types, and values. A profiler reports key count, nesting depth, type distribution, null counts, string lengths, and number ranges. This is essential for understanding unfamiliar datasets, detecting anomalies, and planning data transformations.
For arrays of objects, this tool analyzes each field across all records: presence rate, null percentage, distinct value count, and type consistency. This is invaluable for data engineers evaluating API responses, validating ETL output, or assessing data quality before loading into a warehouse.
[
{"name":"Alice","age":30,"city":null},
{"name":"Bob","age":null,"city":"NYC"},
{"name":"Charlie","age":25}
]
Records: 3 Fields: name, age, city name: 100% present, 0% null type: string (3 unique) age: 100% present, 33% null type: number (range: 25-30) city: 67% present, 33% null type: string (1 unique)
Get the most out of this tool
- Profile API responses to discover undocumented null fields before building your data model.
- Check type consistency across array elements — mixed types in the same field indicate data quality issues.
- Use profiling results to generate JSON Schema rules: presence rates tell you which fields should be "required."
JSON data profiling for developers
Data profiling is the process of examining a dataset to understand its structure, content, and quality. For JSON data, profiling reveals the set of keys present across all records, the type of each value (string, number, boolean, null), the presence rate of each field (how often it appears), the uniqueness of values, the range of numeric values, and the distribution of string lengths. This metadata is essential for designing data pipelines, schemas, and APIs.
Presence rate (also called "fill rate" or "completeness") is one of the most important profiling metrics. If a field appears in only 30% of records, it is optional — your schema must handle its absence. If a field appears in 99.8% of records, it is effectively required — the 0.2% absence is likely a data quality issue worth investigating. Profiling reveals these rates from a sample without manual inspection of each record.
Type consistency is another critical insight from profiling. If a field is a string in 90% of records but a number in 10%, you have a data quality problem — or a polymorphic API where the field serves dual purposes. The profiler reports each field's type distribution, showing "80% string, 15% number, 5% null" for mixed-type fields. This guides decisions about data cleaning, type coercion, and schema design.
Numeric range analysis shows the minimum, maximum, mean, and percentile distribution of numeric fields. Is the "age" field ever negative? Does "quantity" ever exceed 10,000? Does "price" have values in the cents range suggesting it should be an integer in cents? Range analysis catches values that are statistically impossible (negative ages) or unexpected (zero prices), flagging data quality issues before they propagate downstream.
Unique value counts and cardinality information tells you whether a field behaves like an enum (few unique values), an identifier (many unique values approaching the record count), or free text (many unique values with no pattern). High-cardinality string fields might need full-text search indexing. Low-cardinality fields are good candidates for filter facets in search UIs and GROUP BY in analytics queries. Cardinality analysis informs index design and query optimization.
Data quality issues revealed by profiling include empty strings (semantically different from null but often treated the same), whitespace-only values, leading and trailing whitespace in string fields, values outside expected ranges, impossible combinations (end_date before start_date), and duplicate records in fields that should be unique. Profiling a new dataset before processing it catches these issues early, preventing them from corrupting downstream systems.
When developers use this tool
Additional frequently asked questions
How large a JSON dataset can the profiler analyze?
The profiler processes files in a Web Worker for inputs over 100KB, keeping the UI responsive. For datasets in the megabyte range, profiling runs efficiently by streaming through the data. Very large files (tens of megabytes) should be profiled via the REST API at api.jsonfmt.dev/profile which handles server-side processing without browser memory limits.
What statistics does the profiler provide for string fields?
For string fields, the profiler reports: unique value count (cardinality), minimum and maximum string length, average length, most frequent values (top-5), and presence rate (percentage of records where the field is non-null). For fields with low cardinality (under 20 unique values), all unique values are listed.
Can I profile a JSON object instead of an array?
Yes. For a single JSON object, the profiler reports the top-level keys, their types, and for nested objects, recursive statistics. For a JSON array, it analyzes each element and produces aggregate statistics across all elements. Both inputs produce useful profile information, though array profiling yields richer statistics due to the multiple samples.
Does profiling reveal PII or sensitive data?
The profiler shows sample values and frequent values for each field, which could include PII if present in the data. Before profiling production data containing personal information, either anonymize the data first or ensure you are working in a secure, access-controlled environment. The profiler runs entirely in your browser — data is never sent to a server.