January 16, 2026
Quack to the future
Why DuckDB is my first choice for data processing
DuckDB fans are quacking: simple, fast, and in your browser
TLDR: The author crowns DuckDB as the go-to for fast, simple data work on one machine. Comments cheer querying files and browser-ready embedding, while skeptics fret about full scans and Postgres/Citus comparisons—signaling a shift from bulky clusters to lightweight tools you can ship and run anywhere.
The post boldly crowns DuckDB the one‑machine hero for crunching big spreadsheets and logs, and the comments absolutely light up. The loudest cheers? “SQL on CSV and JSON is pretty sweet”—folks love that it feels like magic to query files without spinning up a heavyweight database. Others dream big: one commenter ponders column magic inside Postgres or using Citus, but wonders if DuckDB is the smarter stepping stone. Another wants DuckDB on Android with a familiar Java API—because yes, people want this thing everywhere. The hype peaks when devs rave about embedding analytics straight into apps—and even running it in the browser with WebAssembly—name‑dropping notebook ideas like marimo and the DuckDB web shell.
Then the drama: a skeptic asks if no indexes means painfully slow full scans. DuckDB fans clap back with “it’s optimized for analytics,” saying scanning is the whole point for medium data and that it’s still blisteringly fast. The post’s shade at cluster tools like Spark sparks memes—“Quack cures data pain,” “Bye, cluster bros”—as people love the zero‑setup vibe: pip install and go. Whether you’re team “use Postgres + extensions” or team “ship a tiny DuckDB and run it everywhere,” the mood is clear: DuckDB is the new default for fast, simple data work, with a side of swagger and a lot of quacks.
Key Points
- •DuckDB is an open-source, in-process SQL engine optimized for analytics workloads.
- •The article claims DuckDB can be 100–1,000x faster than identical queries on SQLite or PostgreSQL for analytics tasks.
- •DuckDB is easy to install (single binary, pip with no dependencies) and well-suited to CI/testing and rapid local development.
- •It supports querying data directly from files (CSV, Parquet, JSON), Amazon S3, and the web, with a UI and web shell available.
- •DuckDB’s SQL dialect includes features like EXCLUDE, COLUMNS (regex-based selection), QUALIFY, aggregate modifiers on window functions, and function chaining.