100% Client-Side Processing — Zero Server, Zero Upload, Zero Tracking

SQL Studio: Query CSV and JSON with SQLite

Drop CSV & JSON → Build SQLite tables in WebAssembly → Query with a pro SQL editor → Visualize → Export anywhere. The complete offline data lab, entirely in your browser.

WebAssembly SQLite CSV & JSON Import Live Charts CSV / JSON / PDF / ZIP Zero Data Sent

Drop CSV or JSON files here

or click to browse — files stay 100% local, never uploaded

SQL Query Editor Ctrl+Enter to Run
Ready
— ms
— rows
0 tables
Ctrl+Enter runs query

Query results will appear here.
Run a SQL query to get started.

Run a query and click Render Chart
to visualize your data.

All exports are generated 100% in your browser. No data is transmitted to any server.

Complete Usage Guide

1

Import Your Data

Drag & drop any .csv or .json file onto the drop zone. The tool auto-detects column types and creates a real SQLite table in WebAssembly memory. Or click Inject Mock Data to start instantly.

2

Write SQL

Use the professional Ace code editor with SQL syntax highlighting. Click any table in the sidebar to auto-generate a preview query. Press Ctrl+Enter or click Run Query.

3

Visualize Results

Switch to the Chart tab to instantly render Bar, Line, or Doughnut charts from your query results. Choose which columns map to labels and values.

4

Export Anywhere

Download results as CSV, JSON, a full SQL Dump, a ZIP archive, or a formatted PDF report — all generated in-browser, zero data sent.

WebAssembly SQLite Engine

Powered by sql.js, a port of SQLite compiled to WebAssembly. This gives you a real, standards-compliant SQL database engine running at near-native speed inside your browser tab. Supports all SQLite SQL syntax including JOINs, subqueries, window functions, and aggregate functions.

Smart CSV/JSON Parser

Uses PapaParse to handle files up to hundreds of megabytes. Auto-detects column data types (INTEGER, REAL, TEXT) by sampling the first rows, creates properly typed SQLite schemas, and provides a progress indicator for large files.

Professional Code Editor

Built on Ace Editor — the same engine powering Cloud9 and CodePen. Enjoy SQL syntax highlighting, code folding, line numbers, auto-indent, bracket matching, and keyboard shortcuts that developers actually expect.

100% Private by Design

Your data never leaves your device. There is no server, no API, no analytics on your queries or files. SQL Studio ULTRA MAX processes everything using browser-native WebAssembly and JavaScript. Close the tab — your data is gone.

Frequently Asked Questions

How large a CSV file can I load?

SQL Studio ULTRA MAX uses streaming parsing via PapaParse and processes files in chunks to stay memory-efficient. In practice, files up to 50–100 MB work smoothly on modern devices. Very large files (>100 MB) may slow down depending on your device's RAM. For best performance, filter your data first using the SQL query engine rather than loading your entire dataset.

Can I load multiple files and JOIN them?

Yes! Each dropped file becomes a separate SQLite table named after the file. Drop users.csv and orders.csv and immediately write: SELECT u.name, COUNT(o.id) FROM users u JOIN orders o ON u.id = o.user_id GROUP BY u.name. Full multi-table SQL is supported.

What SQL syntax is supported?

SQL Studio runs a full SQLite engine, so all SQLite-compatible SQL is supported: SELECT, INSERT, UPDATE, DELETE, CREATE TABLE, DROP TABLE, WITH (CTEs), window functions (ROW_NUMBER, RANK, LEAD, LAG), JSON functions, datetime functions, GROUP BY, HAVING, ORDER BY, LIMIT, OFFSET, subqueries, and all standard aggregate functions.

Does this work offline after the first load?

Yes. SQL Studio ULTRA MAX is a Progressive Web App (PWA). After the first visit, all assets (including the WebAssembly SQLite binary) are cached by the service worker. You can use it fully offline — on a plane, in a basement, anywhere — without any internet connection.

What JSON format does it accept?

The tool accepts two JSON shapes: an array of objects (most common — e.g. [{"id":1,"name":"Alice"}]) where each object's keys become columns; or a root-level object with an array property (the tool will find the first array and use that). Nested objects are automatically JSON-stringified into a TEXT column.

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