Use the free JSON to SQL INSERT Converter below: your data never leaves your browser — all work happens on your device, and nothing is uploaded to any server.
Why JSON and SQL Keep Meeting
Modern apps speak JSON. Databases speak SQL. Sooner or later, data born as JSON has to live in a table: an API export, a log file, a spreadsheet saved as JSON, or a test dataset you built by hand. Typing INSERT statements for that data by hand is slow and error-prone. One missed quote breaks the whole batch, and a value like O’Brien quietly corrupts a row if you forget to double the quote. A converter removes that friction. You paste the JSON, pick a table name, and get clean statements you can run right away.
The tool above handles the boring parts for you. It reads every object in your array and collects all the keys it finds, so rows with different fields still land in one consistent column list. Numbers stay unquoted, true and false become TRUE and FALSE, missing values become NULL, and text is quoted with single quotes doubled the way SQL expects. Nested objects and arrays are stored as JSON text, which keeps the data intact instead of dropping it silently.
How to Use It
Start with a JSON array of objects, like the sample the tool loads for you. Each object is one row. If you have a single object instead of an array, paste it anyway: the tool wraps it into one row. Type the table name you want, then choose between one multi-row INSERT or one statement per row. Multi-row INSERT is shorter and faster for large imports. One statement per row is easier to debug, because a bad row fails on its own instead of failing the whole batch.
Check the output before you run it. Look at the column list first: if a typo created two keys like “city” and “City”, they become two columns, and that is usually a sign to fix the source data. Also check NULLs. A missing key and an explicit null both become NULL, which is honest, but if a column you expected is all NULLs, your key name probably does not match. Copy the result or download it as a .sql file, then run it in your database client.
Types, Quotes, and Edge Cases
SQL quoting rules are simple once you see them, but they punish carelessness. Text goes in single quotes, and any single quote inside the text is doubled. That is why O’Brien becomes ‘O”Brien’. Double quotes are not the escape character in standard SQL, so the tool never uses them for text. Dates usually arrive as text like “2026-05-01”, and the tool keeps them as quoted text, which most databases accept for date columns. If your column is a true date type and the import complains, cast it in the statement or check the format first.
Numbers deserve a second look too. The tool passes numbers through untouched, which is right for integers and decimals. But a value like “007” in JSON is text, not a number, and it stays quoted, which preserves the leading zeros. Phone numbers, postcodes, and product codes behave the same way: if the leading zero matters, keep the value as text in your JSON. Large integers beyond the safe range in some languages can lose precision when parsed, so for IDs with more than fifteen digits, quote them as text.
When This Tool Fits, and When It Does Not
This converter fits one-off imports, test data, and small migrations: a few hundred rows, a table you control, a script you want to inspect before running. It is also a good teaching aid, because the output shows exactly how JSON maps to rows and columns. It does not fit huge datasets. Pasted text in a browser has practical limits, and a million-row import belongs in a proper loader or a database copy command, not in a text box. It also does not create the table for you: if the table does not exist yet, write a CREATE TABLE statement with the types you want first, then run the INSERT output.
Privacy is the quiet advantage here. Your data never leaves your browser. Nothing is uploaded, nothing is stored on a server, and nothing is logged. That matters when the JSON holds customer names, internal IDs, or test data copied from a production system. Run it on your own machine, copy the output, and close the tab. The same local-only design applies to every converter on this site, so you can use them on sensitive data without wondering where it went.
Common Mistakes to Avoid
The most common mistake is pasting JSON with a trailing comma, which is invalid JSON and will be rejected. The tool tells you the parse error, so fix the comma and try again. Another is mixing object shapes wildly: an array where one row has “price” and another has “cost” produces both columns, with NULLs filling the gaps. That is correct behaviour, but it is worth a second look. Finally, never run generated SQL on a production database without a backup or a transaction. Wrap the output in BEGIN and COMMIT in your client if you can, so a mistake rolls back cleanly instead of landing halfway.
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