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DuckDB — in-process SQL analytics directly over CSV, Parquet, and DataFrames

OSS scale score 307.6OSS health 92Update pace 2,385 commits/30d
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OSS scale score is an unbounded metric that log-compresses and weights Stars, Watchers, Forks, and Contributors. Discovery score is the current OSS scale score minus the score at discovery. Update pace is commits in the last 30 days, growth momentum is the OSS scale score difference within the recent observation window, and OSS health is a 0–100 rating based on available recency, Community Health, and release data.

Stars
41,557
Primary language
C++
License
MIT
Repository last updated
Sep 19, 2026

Overview

DuckDB is an analytical SQL database that runs inside an application process without a separate database server. It can query CSV and Parquet files directly from the `FROM` clause and provides clients for Python, R, Java, Wasm, and other environments. It is well suited to local analytics, transformation, and embedded analytical features.

Features and best fit

Based on official documentation; not hands-on tested · Content checked:

Query CSV and Parquet directly without a load step

File paths can appear directly in SQL queries, reducing the need to import local analytical data into a separate database first.

Sources: [2]

Run rich analytical SQL inside an application

DuckDB supports window functions, complex types, correlated subqueries, and integrations with DataFrames, allowing analytics to be embedded in existing processes.

Sources: [2]

For local analysis, ETL, and embedded OLAP

DuckDB fits data-science exploration, file transformation, batch processing, and application features that need SQL without operating a separate server.

Sources: [2][4]

Separate its OLAP role from server-oriented OLTP

DuckDB is designed for high-performance analytical workloads inside a process. It is not primarily a replacement for transactional databases serving many continuously connected clients.

Sources: [4]

Official sources

  1. [1]duckdb/duckdb repository(2026-09-20)
  2. [2]DuckDB README(2026-09-20)
  3. [3]DuckDB MIT license(2026-09-20)
  4. [4]DuckDB documentation(2026-09-20)
Supplemental curator note

DuckDB is especially useful when you want analytical SQL over local files or DataFrames without operating a separate database server. It should be evaluated primarily as an OLAP/in-process analytics engine rather than as a server-oriented transactional database.

Try it in 3 steps

  1. 1

    Install the DuckDB Python package

    Prepare an in-process DuckDB environment without running a separate server.

    python -m pip install duckdb
  2. 2

    Run a SQL query

    Verify SQL execution inside the Python process.

    python -c "import duckdb; print(duckdb.sql('SELECT 42 AS answer').fetchall())"
  3. 3

    Query Parquet or CSV directly

    Point the FROM clause at a local analytical file without loading it into a database first.

    SELECT * FROM 'sample.parquet' LIMIT 10;
Check the official README

Growth

Growth trends · Last 30 days

41,557 Stars

Trend data is still being collected.

Development activity

Last 90 days · weekly

Commits (last 30 days)
2,385
Open PRs
332
Issues opened
667
Issues closed
533
PRs opened
1,766
PRs merged
1,306

Issues

667 / 533

Jun 23Sep 20
Issues openedIssues closed

Pull requests

1,766 / 1,306

Jun 23Sep 20
PRs openedPRs merged

Maintenance

Median first response
2.3 days
Issue response rate
17.3% (13/75)

Based on up to the 100 newest issues opened by external users in the last 90 days. A first comment from an OWNER, MEMBER, or COLLABORATOR counts as a response; issues whose full comment history cannot be checked are excluded. The median and response rate update weekly.

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GitHub data

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GitHub Topics

  • sql
  • database
  • olap
  • analytics
  • embedded-database
Stars
41,557
Forks
3,809
Watchers
280
Open issues
566
Contributors
345
Primary language
C++
License
MIT
Repository last updated
Sep 19, 2026
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