The Workflow Engine For Data Engineers And Data Scientists
An interview about how the Prefect workflow engine unifies the needs of data engineers and data scientists with a pure Python API Building a data platform that works equally well for data engineering and data science is a task that requires familiarity with the needs of both roles. Data engineering platforms have a strong focus on stateful execution and tasks that are strictly ordered based on dependency graphs. Data science platforms provide an environment that is conducive to rapid experimentation and iteration, with data flowing directly between stages. Jeremiah Lowin has gained experience in both styles of working, leading him to be frustrated with all of the available tools. In this episode he explains his motivation for creating a new workflow engine that marries the needs of data engineers and data scientists, how it helps to smooth the handoffs between teams working on data projects, and how the design lets you focus on what you care about while it handles the failure cases for you. It is exciting to see a new generation of workflow engine that is learning from the benefits and failures of previous tools for processing your data pipelines.
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