# SQL Data Workbench — Variant Guides

For the full tool, see: [https://capytoolkit.com/tools/developer/sql-workbench/](https://capytoolkit.com/tools/developer/sql-workbench/)

## Format Guides

[Query Parquet Files with SQL in Your Browser](https://capytoolkit.com/tools/developer/sql-workbench/query-parquet-files/): Load a .parquet file and run DuckDB SQL queries instantly. Column pruning, filter pushdown, schema inspection, and Parquet export, all in your browser, no install required.
[Query CSV and TSV Files with SQL in Your Browser](https://capytoolkit.com/tools/developer/sql-workbench/query-csv-tsv-files/): Load a .csv or .tsv file and run SQL queries in your browser using DuckDB-WASM. Automatic type inference, delimiter detection, and instant schema preview, with no install needed.
[Query Excel Files with SQL in Your Browser](https://capytoolkit.com/tools/developer/sql-workbench/query-excel-files/): Load an .xlsx file and run SQL queries across all sheets using DuckDB-WASM. Each sheet becomes a separate table, with no install or upload required.
[Query JSON and NDJSON Files with SQL in Your Browser](https://capytoolkit.com/tools/developer/sql-workbench/query-json-ndjson-files/): Load a .json, .jsonl, or .ndjson file and run DuckDB SQL queries on it instantly. Handles both JSON arrays and newline-delimited JSON without conversion.
[Query SQLite Databases in Your Browser](https://capytoolkit.com/tools/developer/sql-workbench/query-sqlite-database/): Load a .sqlite or .db file and run SQL queries against all tables instantly. Every table in your database is available without any server or install.
[Query Apache Arrow and Feather Files with SQL](https://capytoolkit.com/tools/developer/sql-workbench/query-arrow-feather-files/): Load a .arrow or .feather file and run DuckDB SQL queries instantly in your browser. No install, no upload. Arrow IPC files become queryable views in seconds.
[Query Avro Files with SQL in Your Browser](https://capytoolkit.com/tools/developer/sql-workbench/query-avro-files/): Load a .avro file and run SQL queries on it using DuckDB-WASM. The Avro schema stored in the container header sets column types automatically. No conversion is needed.
[Query DBF dBASE Files with SQL in Your Browser](https://capytoolkit.com/tools/developer/sql-workbench/query-dbf-files/): Load a .dbf dBASE file and run SQL queries on it using DuckDB-WASM. Common alongside shapefiles and in legacy GIS and business systems; no install required.
[Query GeoParquet Files with SQL in Your Browser](https://capytoolkit.com/tools/developer/sql-workbench/query-geoparquet-files/): Load a .parquet GeoParquet file and run SQL queries on attribute columns using DuckDB-WASM. Geometry arrives as WKB binary. Query everything else with full SQL.

## SQL Data Workbench: Code Examples

[DuckDB Window Functions](https://capytoolkit.com/tools/developer/sql-workbench/code-examples/#duckdb-window-functions): Write DuckDB window functions in the browser SQL Workbench. OVER, PARTITION BY, ORDER BY, ROWS frames, LAG, LEAD, RANK, DENSE_RANK, and QUALIFY explained with examples.
[DuckDB PIVOT / UNPIVOT](https://capytoolkit.com/tools/developer/sql-workbench/code-examples/#duckdb-pivot-unpivot): Use DuckDB PIVOT and UNPIVOT in the browser SQL Workbench to rotate tables from wide to long and long to wide. Syntax, examples, and gotchas explained.
[DuckDB JSON Queries](https://capytoolkit.com/tools/developer/sql-workbench/code-examples/#duckdb-json-nested): Query nested JSON columns in DuckDB using json_extract, ->, ->>, and dot notation. Works on JSON files and JSON columns in Parquet or CSV loaded in the browser workbench.
[DuckDB UNNEST](https://capytoolkit.com/tools/developer/sql-workbench/code-examples/#duckdb-unnest-arrays): Use DuckDB UNNEST in the browser SQL Workbench to flatten list columns, JSON arrays, and struct arrays into individual rows. Syntax, element indexes, and multi-column unnest explained.
[DuckDB Date & Time](https://capytoolkit.com/tools/developer/sql-workbench/code-examples/#duckdb-date-time-functions): Work with dates and timestamps in the browser SQL Workbench using DuckDB. strptime, date_trunc, date_part, INTERVAL arithmetic, and epoch functions with examples.
[Parquet Export](https://capytoolkit.com/tools/developer/sql-workbench/code-examples/#export-results-parquet): Export any DuckDB query result as a Parquet file directly from the browser SQL Workbench. Typed output, column preservation, and one-click download. No server required.
[SQL for Pandas Users](https://capytoolkit.com/tools/developer/sql-workbench/code-examples/#sql-for-pandas-users): Translate pandas operations to DuckDB SQL in the browser workbench. groupby, merge, query, explode, pivot_table, and more, with side-by-side SQL equivalents.

## SQL Data Workbench Reference

[What Is Parquet Format?](https://capytoolkit.com/tools/developer/sql-workbench/reference/#parquet-format): Parquet is a columnar binary file format for analytical workloads. Learn how Parquet stores data, how it compares to CSV and Arrow, and how DuckDB reads it.
[What Is Apache Arrow Format?](https://capytoolkit.com/tools/developer/sql-workbench/reference/#apache-arrow-format): Apache Arrow is an in-memory columnar data format for zero-copy data sharing between languages and tools. Learn how it differs from Parquet and how DuckDB reads Arrow files.
[What Is DuckDB-WASM?](https://capytoolkit.com/tools/developer/sql-workbench/reference/#duckdb-wasm): DuckDB-WASM is DuckDB compiled to WebAssembly, enabling the DuckDB analytical SQL engine to run in a web browser without a server or install.
[What Is NDJSON?](https://capytoolkit.com/tools/developer/sql-workbench/reference/#ndjson-format): NDJSON (Newline-Delimited JSON) stores one JSON object per line. Learn how it differs from regular JSON, why it is used in log pipelines, and how DuckDB reads it.
[What Is Apache Avro Format?](https://capytoolkit.com/tools/developer/sql-workbench/reference/#avro-format): Apache Avro is a row-oriented binary serialization format with the schema stored inside the container file. Learn how Avro works, where it is used, and how DuckDB reads it.
[What Is Feather Format?](https://capytoolkit.com/tools/developer/sql-workbench/reference/#feather-format): Feather (v2) is the on-disk form of Apache Arrow IPC data, designed for fast random-access reads. Learn how Feather differs from Parquet and how DuckDB reads .feather files.
[What Is DBF dBASE Format?](https://capytoolkit.com/tools/developer/sql-workbench/reference/#dbf-format): DBF (dBASE) is a tabular file format used in ESRI shapefiles and legacy business systems. Learn its structure, encoding quirks, and how DuckDB reads .dbf files.
[What Is Columnar Storage?](https://capytoolkit.com/tools/developer/sql-workbench/reference/#columnar-storage): Columnar storage keeps each column's values together on disk rather than each row's values. Learn why it makes analytical queries faster and which file formats use it.
[What Is GeoParquet Format?](https://capytoolkit.com/tools/developer/sql-workbench/reference/#geoparquet-format): GeoParquet extends Apache Parquet with a standard for storing vector geometry as WKB. Learn its structure, where it comes from, and how DuckDB reads GeoParquet files.