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    <title>beam</title>
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    <updated>2026-08-03T11:47:59+00:00</updated>
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            <entry>
            <id>https://links.biapy.com/links/4559</id>
            <title type="text"><![CDATA[Apache Beam®]]></title>
            <link rel="alternate" href="https://beam.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4559"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Unified Apache Beam Model. The easiest way to do batch and streaming data processing. Write once, run anywhere data processing for mission-critical production workloads.

Apache Beam is a unified programming model for Batch and Streaming data processing.
Apache Beam is a unified model for defining both batch and streaming data-parallel processing pipelines, as well as a set of language-specific SDKs for constructing pipelines and Runners for executing them on distributed processing backends, including Apache Flink, Apache Spark, Google Cloud Dataflow, and Hazelcast Jet. 

- [Beam @ GitHub](https://github.com/apache/beam).]]>
            </summary>
            <updated>2025-08-29T04:36:39+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4560</id>
            <title type="text"><![CDATA[Dataflow]]></title>
            <link rel="alternate" href="https://cloud.google.com/dataflow/?hl=en" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4560"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Unified stream and batch data processing that&amp;#039;s serverless, fast, and cost-effective.

- [ &amp;quot;CI/CD avec Dataflow dans Google Cloud&amp;quot; au GDG Cloud Nantes @ GDG France&amp;#039;s YouTube :fr: ](https://www.youtube.com/watch?v=BK88_bIoCpc).]]>
            </summary>
            <updated>2025-08-29T04:36:39+00:00</updated>
        </entry>
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