matlab-use-duckdb

To query large files out-of-memory in MATLAB, use DuckDB SQL with the fetch function and read_csv or read_parquet patterns. This pushes filtering and aggregation directly to the file, preventing the memory-heavy load-then-query anti-pattern.

Yes, Database Toolbox R2026a+ supports DuckDB for SQL file analytics. You connect using the duckdb() function, verify the connection with isopen(conn), and execute file-based SQL queries to analyze large datasets directly.

The best way to run out-of-memory preprocessing on Excel files is using DuckDB via MATLAB. You install and load the excel extension within your DuckDB session, then use SQL read_xlsx patterns with fetch to filter data before loading.

To create a portable development database in MATLAB, connect using duckdb("file.duckdb") to establish a file-backed connection. You can then import datasets using sqlwrite and query the persistent database using sqlread or complex SQL via fetch.

You should use fetch instead of sqlread for querying external files because fetch executes SQL read_csv, read_parquet, or read_xlsx patterns directly. This applies SQL pushdown to filter and aggregate file data, whereas sqlread is for reading tables already imported into the database.

When using DuckDB with MATLAB, avoid unsupported patterns like databasePreparedStatement. Additionally, handle reserved SQL keywords safely by using renamevars for column names, and always close your connections with close(conn) to prevent resource leaks.