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duckdb

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Use when analytical SQL must run in-process with no server: Parquet/CSV/JSON/Arrow queried in place, OLAP embedded in an app or notebook, a slow pandas groupby on multi-GB data, or S3/lakehouse data read without downloading. NOT a multi-user analytics server (that is clickhouse-analytics), NOT an app's transactional CRUD store (that is postgresdb).

skills/duckdb/SKILL.md

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Nov 9, 2026
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MIT

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# DuckDB — embedded columnar OLAP, no server

DuckDB is an in-process analytical (OLAP) database: it links into your process like SQLite, but stores
data column-by-column and vectorizes execution for aggregates, joins, and window functions. There is no
server, no port, no daemon — you `pip install duckdb` (or drop one CLI binary) and query. Its killer move
is reading Parquet/CSV/JSON/Arrow **in place**, without a load step, so a folder of files becomes a table.

The fact that drives every routing decision below: DuckDB is **one writer, many readers, single process**.
It is brilliant for analysis on one machine and wrong for multi-user serving or transactional app writes.

Latest stable is **v1.5.3** (released 2026-05-20). Pin the **LTS line (v1.4.x; v1.4.4 LTS shipped
2026-01-26)** for anything long-lived — LTS gets ~1 year of patches and a stable storage format. Use 1.5.x
for greenfield exploration.
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