Convert Parquet to SQLite
Upload a .parquet file and get back a SQLite file. Types are preserved rather than flattened to text. Free, no registration, up to 10MB.
Click to upload or drag and drop
CSV, Excel, SQLite, SQL, DBF (max 10 MB)
How your data is typed in SQLite
Generated from the converter's own SQLite type mapping — this is what it actually emits, not an example.
| Your data | SQLite type |
|---|---|
| Whole number | BIGINT |
| Decimal (money) | NUMERIC(10,2) |
| Floating point | REAL |
| True / false | BOOLEAN |
| Date | DATE |
| Date and time | TIMESTAMP |
| Short text | TEXT |
| Long text | TEXT |
| Binary data | BLOB |
Working from a .parquet file
Parquet is the one source here that arrives with a complete, machine-readable schema, so the conversion is a translation rather than an investigation. The interesting question is what Parquet can express that a SQL table cannot.
- →Lists, structs and maps have no scalar equivalent in a relational target, so those columns are carried across as JSON text instead of being exploded into invented columns.
- →Integer widths are kept rather than widened: int8 and int16 become a 16-bit column and int32 a 32-bit one, so the target schema stays as tight as the file was.
- →A Parquet dataset is usually a directory of part files with partition keys encoded in directory names like year=2024/. One file is read, and a column that exists only as a directory name is not inside it.
- →Rows are streamed in batches of 10,000, so a wide file never has to be held in memory all at once.
What the .sqlite file looks like inside
The output here is a real database rather than a script, so the interesting question is not how to load it but what SQLite did with the types on the way in. Its storage classes are looser than the declarations the file ends up carrying.
- →Names are carried over exactly as they were, with none of the sanitising the script targets apply: a table called Sales Report keeps its space and a column called Order ID keeps its own, so your queries have to quote them.
- →Underneath the declared types sit only five storage classes, and NUMERIC affinity - what a decimal column gets - is the loosest of them: it normalises inserted text to whatever a plain INTEGER or REAL column would hold. The precision and scale in the schema are for a human reading it, not a guarantee SQLite enforces on the bytes; the Writing notes below cover what that means for money specifically.
- →Declared type and stored type are different things here: a BOOLEAN column holds the integers 1 and 0, and a DATE column holds ISO text, because those are the storage classes SQLite actually has. Every client reads them back the same way.
Reading Parquet
- →Parquet carries a full schema, so nothing is guessed — types, decimal precision and nullability are read directly.
- →Decimal columns keep their exact precision and scale.
Writing SQLite
- →Columns get real declared types rather than everything being TEXT.
- →Primary keys and NOT NULL constraints are preserved.
- →A money column is declared NUMERIC(10,2), but that declaration does not stop SQLite’s own numeric affinity from converting the inserted value into a float once it lands, so 19.99 can come back with a rounding tail. Pick the DuckDB target instead when the exact decimal has to survive.
What you get
A ZIP containing database.sqlite. If anything about your data needed a judgement call, a _warnings.txt explains it.
Questions
- Is converting Parquet to SQLite free?
- Yes. Files up to 10MB convert free with no account. There is a limit of five conversions per day per IP address.
- Will my Parquet data keep its types in SQLite?
- Yes — that is the point. Whole numbers, decimals, dates and booleans are mapped to real SQLite types rather than everything becoming text. The mapping table above shows exactly what you get.
- Do you store my file?
- No. The file is written to a temporary directory, converted, and the directory is deleted as soon as your download has been sent.