<?xml version="1.0" encoding="UTF-8"?>
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    <title>duckdb</title>
    <link rel="self" type="application/atom+xml" href="https://links.biapy.com/guest/tags/614/feed"/>
    <updated>2026-04-21T16:53:04+00:00</updated>
    <id>https://links.biapy.com/guest/tags/614/feed</id>
            <entry>
            <id>https://links.biapy.com/links/12256</id>
            <title type="text"><![CDATA[Open DroneLog]]></title>
            <link rel="alternate" href="https://opendronelog.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12256"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Free Open Source Drone Log Analysis and backup tool- Own Your Drone Data.

 Free, open-source drone flight log analyzer for DJI and Litchi. Analyze telemetry, replay 3D flights, track battery health with privacy-first local storage. Desktop &amp;amp; Docker available. 

- [Open DroneLog @ GitHub](https://github.com/arpanghosh8453/open-dronelog).

Related contents:

- [Open DroneLog - Vos logs de drone restent chez vous @ Korben :fr:](https://korben.info/opendronelog-carnet-vol-drone-open-source.html).]]>
            </summary>
            <updated>2026-03-23T15:37:43+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11904</id>
            <title type="text"><![CDATA[Shaper]]></title>
            <link rel="alternate" href="https://taleshape.com/shaper/docs/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11904"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open Source, SQL-driven Data Dashboards powered by DuckDB.

Build analytics dashboards simply by writing SQL.

- [Shaper @ GitHub](https://github.com/taleshape-com/shaper).

Related contents:

- [Digest \#202: Terraform Claude Skills, FinOps FOCUS 1.2, AI Fatigue for Cloud Engineers, and MCP for Web Data Extraction @ DevOps Bulletin](https://www.devopsbulletin.com/p/digest-202-terraform-claude-skills).]]>
            </summary>
            <updated>2026-02-24T07:10:47+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11689</id>
            <title type="text"><![CDATA[msgvault]]></title>
            <link rel="alternate" href="https://www.msgvault.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11689"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Archive a lifetime of email and chat. Offline search, analytics, and AI query over your full message history. Powered by DuckDB.

Your messages are yours. Decades of correspondence, attachments, and history shouldn&amp;#039;t be locked behind a web interface or an API. msgvault downloads a complete local copy and then everything runs offline. Search, analytics, and the MCP server all work against local data with no network access required.

Currently supports Gmail, with WhatsApp and other messaging platforms planned.

- [msgvault @ GitHub](https://github.com/wesm/msgvault).

Related contents:

- [Announcing msgvault: lightning fast private email archive and search system, with terminal UI and MCP server, powered by DuckDB @ Wes McKinney](https://wesmckinney.com/blog/announcing-msgvault/).
- [msgvault - Libérez vos emails de la prison Gmail @ Korben :fr:](https://korben.info/msgvault-archive-email-local.html).]]>
            </summary>
            <updated>2026-04-07T06:52:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11238</id>
            <title type="text"><![CDATA[mooncake]]></title>
            <link rel="alternate" href="https://www.mooncake.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11238"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[a data lakehouse for you and me. managed + real-time Iceberg.

🥮 is real-time + managed Apache Iceberg.
bringing open analytical tables on object store to every team.

pg_mooncake is a ClickHouse alternative for real-time analytics built on Postgres. It turns Postgres into a real-time analytics database by adding:

    Columnar storage (Apache Iceberg, via Moonlink)
    Vectorized execution with DuckDB (via pg_duckdb).

Fast analytics queries require both columnar storage &amp;amp; vectorized execution, and previous Postgres analytics solutions only solved half the problem.

- [mooncake @ GitHub](https://github.com/Mooncake-Labs/).]]>
            </summary>
            <updated>2025-12-15T10:10:52+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10736</id>
            <title type="text"><![CDATA[Duck-UI]]></title>
            <link rel="alternate" href="https://duckui.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10736"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Data is better when we see it!

Duck-UI makes working with data easy. Run SQL queries directly in your browser with DuckDB WASM - no server required!

 Duck-UI is a web-based interface for interacting with DuckDB, a high-performance analytical database system. It features a SQL editor, data import/export, data explorer, query history, theme toggle, and keyboard shortcuts, all running seamlessly in the browser using DuckDB&amp;#039;s WebAssembly (WASM) capabilities. 

- [Duck-UI @ GitHub](https://github.com/ibero-data/duck-ui).]]>
            </summary>
            <updated>2025-10-20T12:12:22+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10722</id>
            <title type="text"><![CDATA[Arc]]></title>
            <link rel="alternate" href="https://basekick.net/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10722"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Fastest Time-Series Database.
 High-performance time-series data warehouse built on DuckDB and Parquet with flexible storage options. 

Time-series data warehouse built for speed. 2.42M records/sec on local NVMe. DuckDB + Parquet + Arrow + flexible storage (local/MinIO/S3). AGPL-3.0 

- [Arc @ GitHub](https://github.com/Basekick-Labs/arc).]]>
            </summary>
            <updated>2025-10-20T06:41:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10697</id>
            <title type="text"><![CDATA[Lance]]></title>
            <link rel="alternate" href="https://lancedb.github.io/lance/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10697"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming.. 

Lance is a modern columnar data format optimized for machine learning and AI applications. It efficiently handles diverse multimodal data types while providing high-performance querying and versioning capabilities.

- [Lance @ GitHub](https://github.com/lancedb/lance).

Related contents:

- [Lance takes aim at Parquet in file format joust @ The Register](https://www.theregister.com/2025/10/14/lance_parquet/).]]>
            </summary>
            <updated>2025-10-17T12:01:52+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10464</id>
            <title type="text"><![CDATA[SQLGlot]]></title>
            <link rel="alternate" href="https://sqlglot.com/sqlglot.html" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10464"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Python SQL Parser and Transpiler.

SQLGlot is a no-dependency SQL parser, transpiler, optimizer, and engine. It can be used to format SQL or translate between 31 different dialects like DuckDB, Presto / Trino, Spark / Databricks, Snowflake, and BigQuery. It aims to read a wide variety of SQL inputs and output syntactically and semantically correct SQL in the targeted dialects.

- [SQLGlot @ GitHub](https://github.com/tobymao/sqlglot).]]>
            </summary>
            <updated>2025-09-30T06:38:31+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10212</id>
            <title type="text"><![CDATA[pg_duckdb]]></title>
            <link rel="alternate" href="https://github.com/duckdb/pg_duckdb" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10212"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[DuckDB-powered Postgres for high performance apps &amp;amp; analytics. :

pg_duckdb integrates DuckDB&amp;#039;s columnar-vectorized analytics engine into PostgreSQL, enabling high-performance analytics and data-intensive applications.

Related contents:

- [Announcing Pg_duckdb Version 1.0 @ MotherDuck](https://motherduck.com/blog/pg-duckdb-release/).]]>
            </summary>
            <updated>2025-09-15T13:36:43+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/466</id>
            <title type="text"><![CDATA[Sirius]]></title>
            <link rel="alternate" href="https://github.com/sirius-db/sirius" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/466"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Sirius is a GPU-native SQL engine. It plugs into existing databases such as DuckDB via the standard Substrait query format, requiring no query rewrites or major system changes. Sirius currently supports DuckDB and Doris (coming soon), other systems marked with * are on our roadmap.]]>
            </summary>
            <updated>2025-08-28T17:14:54+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/661</id>
            <title type="text"><![CDATA[DuckLake]]></title>
            <link rel="alternate" href="https://ducklake.select/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/661"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[DuckLake is an integrated data lake and catalog format

DuckLake delivers advanced data lake features without traditional lakehouse complexity by using Parquet files and your SQL database. It&amp;#039;s an open, standalone format from the DuckDB team.

DuckLake is an open Lakehouse format that is built on SQL and Parquet. DuckLake stores metadata in a catalog database, and stores data in Parquet files. The DuckLake extension allows DuckDB to directly read and write data from DuckLake.

- [Ducklake @ GitHub](https://github.com/duckdb/ducklake).]]>
            </summary>
            <updated>2025-08-28T17:48:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/879</id>
            <title type="text"><![CDATA[DuckDB-DOOM]]></title>
            <link rel="alternate" href="https://github.com/patricktrainer/duckdb-doom" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/879"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Doom-like game using DuckDB.

A 3D first-person shooter game implemented entirely in SQL using DuckDB-WASM.

- [Abusing DuckDB-WASM by making SQL draw 3D graphics (Sort Of) @ 👋 🌎 Hey, Earth!](https://www.hey.earth/posts/duckdb-doom).]]>
            </summary>
            <updated>2025-08-28T18:24:29+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1211</id>
            <title type="text"><![CDATA[Quacklytics]]></title>
            <link rel="alternate" href="https://github.com/xz3dev/quacklytics" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1211"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Quacklytics is an open-source analytics service built using DuckDB and designed to run analytical queries directly inside your browser. It provides a seamless, lightweight, and high-performance way to process your data without the need for expensive server-side compute resources.]]>
            </summary>
            <updated>2025-08-28T19:18:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1294</id>
            <title type="text"><![CDATA[MDS-in-a-box]]></title>
            <link rel="alternate" href="https://mdsinabox.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1294"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Monte Carlo simulation of the NBA season, leveraging dbt, duckdb and evidence.dev.

A fast, free and open-source Modern Data Stack (MDS) that can be fully deployed on your laptop or to a single machine.

This project implements a sports Monte Carlo simulator using duckdb, dbt, and evidence. The project is built and run about once per day in a github action. You can learn more about this on the original blog post or on the about page.

- [MDS-in-a-box @ GitHub](https://github.com/matsonj/nba-monte-carlo).]]>
            </summary>
            <updated>2025-08-28T19:32:03+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1738</id>
            <title type="text"><![CDATA[Tailpipe]]></title>
            <link rel="alternate" href="https://tailpipe.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1738"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[select * from logs;

Open source SIEM for instant log insights, powered by DuckDB. Analyze millions of events in seconds, right from your terminal.

- [Tailpipe @ GitHub](https://github.com/turbot/tailpipe).]]>
            </summary>
            <updated>2025-08-28T20:45:47+00:00</updated>
        </entry>
    </feed>
