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    <title>apache-iceberg</title>
    <link rel="self" type="application/atom+xml" href="https://links.biapy.com/guest/tags/1011/feed"/>
    <updated>2026-09-15T12:01:37+00:00</updated>
    <id>https://links.biapy.com/guest/tags/1011/feed</id>
            <entry>
            <id>https://links.biapy.com/links/13450</id>
            <title type="text"><![CDATA[StarRocks]]></title>
            <link rel="alternate" href="https://www.starrocks.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/13450"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A High-Performance Analytical Database.

The world&amp;#039;s fastest open query engine for sub-second analytics both on and off the data lakehouse. With the flexibility to support nearly any scenario, StarRocks provides best-in-class performance for multi-dimensional analytics, real-time analytics, and ad-hoc queries. A Linux Foundation project.

- [StarRocks @ GitHub](https://github.com/StarRocks/StarRocks).]]>
            </summary>
            <updated>2026-07-29T13:40:47+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12586</id>
            <title type="text"><![CDATA[Project Nessie]]></title>
            <link rel="alternate" href="https://projectnessie.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12586"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Transactional Catalog for Data Lakes with Git-like semantics.

Nessie supports Iceberg Tables/Views. Additionally, Nessie is focused on working with the widest range of tools possible, which can be seen in the feature matrix.

- [Project Nessie @ GitHub](https://github.com/projectnessie/nessie/).

Related contents:

- [How to Build an Open Source Data Lake for Batch Ingestion @ freeCodeCamp](https://www.freecodecamp.org/news/how-to-build-an-open-source-data-lake-for-batch-ingestion/#heading-nessie).]]>
            </summary>
            <updated>2026-04-21T05:47:33+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/11209</id>
            <title type="text"><![CDATA[Icebird]]></title>
            <link rel="alternate" href="https://github.com/hyparam/icebird" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11209"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[JavaScript Iceberg Client.

Icebird is a library for reading Apache Iceberg tables in JavaScript. It is built on top of hyparquet for reading the underlying parquet files.]]>
            </summary>
            <updated>2025-12-12T07:21:50+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10890</id>
            <title type="text"><![CDATA[pg_lake]]></title>
            <link rel="alternate" href="https://github.com/Snowflake-Labs/pg_lake" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10890"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Postgres with Iceberg and data lake access.

pg_lake integrates Iceberg and data lake files into Postgres. With the pg_lake extensions, you can use Postgres as a stand-alone lakehouse system that supports transactions and fast queries on Iceberg tables, and can directly work with raw data files in object stores like S3.

Related contents:

- [Postgres Is the Gateway Drug @ Vignesh Ravichandran](https://viggy28.dev/article/postgres-gateway-drug/).]]>
            </summary>
            <updated>2026-03-23T16:36:25+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10426</id>
            <title type="text"><![CDATA[RisingWave]]></title>
            <link rel="alternate" href="https://risingwave.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10426"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Real-Time Event Streaming Platform.
Streaming CDC, stream processing, low-latency serving, and Iceberg management. 

RisingWave is a real-time event streaming platform designed to offer the simplest and most cost-effective way to process, analyze, and manage real-time event data — with built-in support for the Apache Iceberg™ open table format. It provides both a Postgres-compatible SQL interface and a DataFrame-style Python interface.

RisingWave can ingest millions of events per second, continuously join and analyze live streams with historical data, serve ad-hoc queries at low latency, and persist fresh, consistent results to Apache Iceberg™ or any other downstream system.

- [RisingWave @ GitHub](https://github.com/risingwavelabs/risingwave).

Related contents:

- [The Case for Hybrid Cache for Object Stores @ Data Engineer Things&amp;#039; Medium](https://blog.dataengineerthings.org/the-case-for-hybrid-cache-for-object-stores-4b1f02ec6c9a).
- [You Gotta Push If You Wanna Pull @ Gunnar Morling](https://www.morling.dev/blog/you-gotta-push-if-you-wanna-pull/).]]>
            </summary>
            <updated>2025-12-08T13:43:05+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10258</id>
            <title type="text"><![CDATA[Bauplan]]></title>
            <link rel="alternate" href="https://www.bauplanlabs.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10258"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Your data lakehouse, built like software.

Bauplan is a cloud-native lakehouse platform for engineering teams who treat data like software.
Ship pipelines without managing infrastructure, using a specialized Python runtime, Git-for-Data built on Apache Iceberg, and just a few simple APIs.

Related contents:

- [Bauplan: Operate your lakehouse with zero infrastructure @ Data Engineer Things](https://blog.dataengineerthings.org/bauplan-operate-your-lakehouse-with-zero-infrastructure-f15a24ca33a9).]]>
            </summary>
            <updated>2025-09-18T05:55:14+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1110</id>
            <title type="text"><![CDATA[OLake]]></title>
            <link rel="alternate" href="https://olake.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1110"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Fastest way to Replicate your Database data in Data Lake.
OLake makes data replication faster by parallelizing full loads, leveraging change streams for real-time sync, and pulling data in a database-native format for efficient ingestion.

Fastest open-source tool for replicating Databases to Apache Iceberg or Data Lakehouse. ⚡ Efficient, quick and scalable data ingestion for real-time analytics. Supporting Postgres, MongoDB and MySQL 

- [OLake @ GitHub](https://github.com/datazip-inc/olake).

Related contents:

- [Change Data Capture Tools @ Dev Genius&amp;#039; Medium](https://blog.devgenius.io/change-data-capture-tools-c0e4ee4434ac).]]>
            </summary>
            <updated>2025-08-28T19:02:48+00:00</updated>
        </entry>
    </feed>
