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    <title>data-warehouse</title>
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    <updated>2026-09-21T00:47:56+00:00</updated>
    <id>https://links.biapy.com/guest/tags/849/feed</id>
            <entry>
            <id>https://links.biapy.com/links/13455</id>
            <title type="text"><![CDATA[Semarchy]]></title>
            <link rel="alternate" href="https://semarchy.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/13455"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[native snowflake MDM.]]>
            </summary>
            <updated>2026-07-29T15:32:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/13452</id>
            <title type="text"><![CDATA[Snowflake]]></title>
            <link rel="alternate" href="https://www.snowflake.com/en/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/13452"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Snowflake is a cloud-based data warehousing platform designed to provide a flexible, scalable, and high-performance solution for storing, processing, and analyzing large volumes of data. It leverages a unique architecture that separates storage and compute resources, allowing users to scale each independently.]]>
            </summary>
            <updated>2026-07-29T14:22:23+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12647</id>
            <title type="text"><![CDATA[Rocky]]></title>
            <link rel="alternate" href="https://rocky-data.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12647"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A compiled SQL transformation engine in Rust. Type-safe compilation, column-level lineage, and an AI intent layer — for data pipelines that don&amp;#039;t break.

- [Rocky @ GitHub](https://github.com/rocky-data/rocky).]]>
            </summary>
            <updated>2026-04-29T13:15:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11631</id>
            <title type="text"><![CDATA[Microsoft Fabric]]></title>
            <link rel="alternate" href="https://www.microsoft.com/en-us/microsoft-fabric" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11631"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Data Analytics Platform.

Related conttens:

- [Qu&amp;#039;est-ce que Microsoft Fabric ? @ datacamp :fr:](https://www.datacamp.com/fr/blog/what-is-microsoft-fabric).]]>
            </summary>
            <updated>2026-01-27T11:05:00+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/10721</id>
            <title type="text"><![CDATA[Code-First CDC to ClickHouse with Debezium, Redpanda, and MooseStack]]></title>
            <link rel="alternate" href="https://github.com/514-labs/debezium-cdc" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10721"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Easy-to-run demo of a CDC pipeline using Debezium (Kafka Connect), PostgreSQL, Redpanda, and ClickHouse.]]>
            </summary>
            <updated>2025-10-20T06:22:14+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/809</id>
            <title type="text"><![CDATA[Apache Doris]]></title>
            <link rel="alternate" href="https://doris.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/809"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open source data warehouse for real time data analytics.

Apache Doris is an easy-to-use, high-performance and real-time analytical database based on MPP architecture, known for its extreme speed and ease of use. It only requires a sub-second response time to return query results under massive data and can support not only high-concurrency point query scenarios but also high-throughput complex analysis scenarios.

- [Apache Doris @ GitHub](https://github.com/apache/doris).]]>
            </summary>
            <updated>2025-08-28T18:12:23+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1339</id>
            <title type="text"><![CDATA[pg_mooncake]]></title>
            <link rel="alternate" href="https://github.com/Mooncake-Labs/pg_mooncake" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1339"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[1000x Faster Analytics in Postgres.  Postgres-native Data Warehouse.

pg_mooncake is a Postgres extension that adds columnar storage and vectorized execution (DuckDB) for fast analytics within Postgres. Postgres + pg_mooncake ranks among the top 10 fastest in ClickBench.

Related contents:

- [Postgres Is the Gateway Drug @  Vignesh Ravichandran](https://viggy28.dev/article/postgres-gateway-drug/).]]>
            </summary>
            <updated>2026-03-23T16:35:46+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1462</id>
            <title type="text"><![CDATA[PeerDB]]></title>
            <link rel="alternate" href="https://www.peerdb.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1462"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Fast, Simple and a cost effective tool to replicate data from Postgres to Data Warehouses, Queues and Storage.

PeerDB is an ETL/ELT tool built for PostgreSQL. We implement multiple Postgres native and infrastructural optimizations to provide a fast, reliable and a feature-rich experience for moving data in/out of PostgreSQL.

- [PeerDB @ GitHub](https://github.com/PeerDB-io/peerdb).

Related contents:

- [Reliably Replicating Data Between PostgreSQL and ClickHouse Part 1 - PeerDB Open Source @ BenjaminWootton.com](https://benjaminwootton.com/insights/clickhouse-peerdb-cdc/).
- [Postgres Is the Gateway Drug @ Vignesh Ravichandran](https://viggy28.dev/article/postgres-gateway-drug/).]]>
            </summary>
            <updated>2026-03-23T16:36:55+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3382</id>
            <title type="text"><![CDATA[MotherDuck]]></title>
            <link rel="alternate" href="https://motherduck.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3382"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Ducking Simple Data Warehouse based on DuckDB.

- [Your October Dose of Data - October 2024 @ Data Council](https://mailchi.mp/datacouncil/october-2024).]]>
            </summary>
            <updated>2025-08-29T01:20:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3399</id>
            <title type="text"><![CDATA[Materialize]]></title>
            <link rel="alternate" href="https://materialize.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3399"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Cloud Operational Data Store. Use SQL to transform, deliver, and act on fast-changing data.

Materialize is a cloud-native data warehouse purpose-built for operational workloads where an analytical data warehouse would be too slow, and a stream processor would be too complicated.

Using SQL and common tools in the wider data ecosystem, Materialize allows you to build real-time automation, engaging customer experiences, and interactive data products that drive value for your business while reducing the cost of data freshness.

- [Materialize @ GitHub](https://github.com/MaterializeInc/materialize).]]>
            </summary>
            <updated>2025-08-29T01:22:59+00:00</updated>
        </entry>
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