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In contrast to this common, centralized approach, a data mesh architecture calls for responsibilities to be distributed to the people closest to the data. Signals around the quality and integrity of the data are essential if people are to understand and trust it. Examples include public cloud vendors like AWS, Azure, and GCP.
For example, let’s take Airflow , AWS SageMaker pipelines. We’re building on top of Hamilton, which is an open-source framework for describing data flows. Stefan: Back in 2019. Like they didn’t have to think about, you know, dataobservability, but look, if you provided those data, we captured things about it.
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