MATLAB Database Toolbox Interface to DuckDB

MATLAB Database Toolbox Interface to DuckDB

Use when working with DuckDB databases from MATLAB using Database Toolbox. DuckDB is an embedded analytical database engine that ships with Database Toolbox starting in R2026a. It enables SQL-based analytics on files, out-of-memory data preprocessing, and portable development databases — all without external database server configuration.

When to Use This Skill

• Connecting to a DuckDB database (in-memory or file-based)
• Creating a new DuckDB database file for development workflows
• Querying CSV, Parquet, or JSON files directly with SQL
• Reducing or profiling large data before analysis
• Using DuckDB as a non-math operations engine (filter, aggregate, deduplicate, string cleanup, type cast, sort, sample)
• Installing and using DuckDB extensions
• Persisting file data into a .duckdb file for repeated queries
• User mentions keywords: DuckDB, duckdb(), analytical engine, embedded database, large CSV, large Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL

When NOT to Use

• Single file that fits in memory — use native MATLAB I/O (readtable/readmatrix)
• Multi-file workflows — use datastore/tall first; DuckDB glob is the fallback, not the default
• Data exceeds memory and no SQL reduction will make it fit — use datastore/tall for streaming
• Math-heavy or numerically sensitive operations (normalize, windowed stats, outlier detection) — hand off to MATLAB after reduction
• Connecting to MySQL, PostgreSQL, SQLite, or other external databases — use their native interfaces or JDBC/ODBC
• Object-relational mapping — use ORM (ormread/ormwrite with Mappable classes)
• MongoDB, Cassandra, or Neo4j — use their dedicated Database Toolbox interfaces

On-Load Protocol

When this skill is loaded into a session where code already exists:

1. Audit the data pipeline — Identify how data enters the workflow:

   • Is data read via MATLAB I/O (readtable, readmatrix, readcell, readtimetable, parquetread, xlsread, csvread)?
   • Is data then written to DuckDB with sqlwrite before querying?
   • If yes: this is the load-then-query anti-pattern. DuckDB can likely read the source file directly via read_csv, read_parquet, or read_xlsx (excel extension).

2. Evaluate each data source against the decision framework:

   • Can DuckDB read this file type directly? (CSV, Parquet, JSON, Excel via extension)
   • Can filtering/aggregation be pushed into the SQL read?
   • Is the MATLAB I/O step a performance bottleneck?

3. Recommend architectural changes — Do not limit review to API correctness. Propose replacing readtable/xlsread + sqlwrite + query chains with fetch(conn, "SELECT ... FROM read_csv/read_parquet/read_xlsx(...)").