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    <title>apache-arrow</title>
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    <updated>2026-08-01T23:19:38+00:00</updated>
    <id>https://links.biapy.com/guest/tags/1657/feed</id>
            <entry>
            <id>https://links.biapy.com/links/13304</id>
            <title type="text"><![CDATA[databow]]></title>
            <link rel="alternate" href="https://docs.columnar.tech/databow/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/13304"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[databow is a command-line tool for querying databases.

A command-line tool for querying databases via ADBC.

- [databow @ GitHub](https://github.com/columnar-tech/databow).]]>
            </summary>
            <updated>2026-07-15T08:43:29+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12892</id>
            <title type="text"><![CDATA[Murr]]></title>
            <link rel="alternate" href="https://github.com/murrdb/murr" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12892"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Sub-millisecond cache for ML/AI workloads. Parquets in, Arrow-Flight out. 

Murr is a caching layer for ML/AI data serving that sits between your batch data pipelines and inference apps.

A RocksDB-based NVMe/S3 cache for AI inference workloads. A faster Redis replacement, optimized for batch low-latency zero-copy reads and writes.]]>
            </summary>
            <updated>2026-06-02T06:37:41+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/3263</id>
            <title type="text"><![CDATA[Vortex]]></title>
            <link rel="alternate" href="https://github.com/spiraldb/vortex" />
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            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[&amp;quot;The LLVM of columnar file formats&amp;quot;. A toolkit for working with compressed Arrow on-disk, in-memory, and over-the-wire. 

Vortex is a toolkit for working with compressed Apache Arrow arrays in-memory, on-disk, and over-the-wire.

Vortex is designed to be to columnar file formats what Apache DataFusion is to query engines (or, analogously, what LLVM + Clang are to compilers): a highly extensible &amp;amp; extremely fast framework for building a modern columnar file format, with a state-of-the-art, &amp;quot;batteries included&amp;quot; reference implementation.]]>
            </summary>
            <updated>2025-08-29T01:00:50+00:00</updated>
        </entry>
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