DuckDB and SQLite
This document introduces DuckDB and SQLite, two file-based relational databases used in analytics.
Both use SQL. They serve different purposes.
DuckDB: Analytics-Oriented SQL Engine
DuckDB is:
- file-based
- designed for analytics
- optimized for columnar queries
- tightly integrated with dataframes and files
DuckDB is especially good for:
- analytics workloads
- querying CSV, Parquet, and DataFrames
- local data exploration
- teaching SQL for analytics
Why We Use DuckDB
DuckDB:
- performs well on analytic queries
- works naturally with pandas
- supports modern SQL features
- avoids server setup
SQLite vs DuckDB (Conceptual Comparison)
SQLite:
- application storage
- transactional workloads
- embedded everywhere
DuckDB:
- analytics and exploration
- columnar execution
- data science workflows
Both:
- use SQL
- store data locally
- are excellent learning tools
How This Transfers to Hosted Databases
Skills learned with SQLite and DuckDB transfer directly to:
- PostgreSQL
- MySQL
- SQL Server
- cloud data warehouses
The SQL concepts are the same. Only scale and deployment differ.
SQLite: The Embedded Relational Database
SQLite is:
- file-based
- embedded
- extremely widely deployed
It is commonly used in:
- browsers
- mobile apps
- desktop software
- internal tools
SQLite emphasizes:
- simplicity
- reliability
- portability
For many applications, it replaces the need for a server database.
Use Cases
- SQLite shows how pervasive relational data is
- DuckDB is ideal for analytics workflows
- File-based databases are realistic proxies
- SQL skills transfer across systems
Assist with:
- querying relational data
- combining SQL and Python
- producing analytic results from structured tables