Data Pipeline Evolution
The discussion highlights the limitations of Airflow as a data orchestrator, emphasizing the need for a more streamlined approach to data pipelines. With a focus on machine learning, the importance of data transformation is underscored, revealing that much of the process involves preparing data rather than modeling. Insights from the evolution of Airflow at a major tech company provide a unique perspective on the challenges faced by data engineers, suggesting a need for greater synergy in the modern data stack.In this clip
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Open Source Startup Podcast
E71: Mage & Replacing Airflow
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