![]() Then we compared Argo Workflows' container-native characteristics to Prefect's Python API and various orchestration tools. We started with the basics of workflow orchestration and what it takes to coordinate data pipelines. In this post, we compared Argo Workflows and Prefect side-by-side. Argo Workflows and Prefect model these dependencies as Directed Acyclic Graphs (DAGs). Running them based on dependencies - how they relate to each other - is more complicated, but it's what you need to organize a data pipeline properly. Running tasks in a specific order is one thing. Which workflow orchestrator is best for your data pipelines? How do these popular tools approach the problem of coordinating tasks? Defining Tasks Dependencies with DAGs Prefect's API makes it possible to orchestrate any Python code. For example, Argo Workflows is flexible enough for organizations to use it for CI/CD. You can use an orchestrator for many different applications, and Prefect and Argo Workflows are two examples that aren't limited to data pipelines. It has tools for defining a set of related steps, establishing relationships between them, and scheduling their execution. You use an orchestrator to start, stop, and organize tasks. We'll compare their features, how easy they are to get started with, and help you decide which is best for your data pipelines. Which one of these workflow orchestrators is the best fit for you? Two of the more popular choices are Prefect and Argo Workflows. ![]() Most of these data pipelines start with a workflow orchestrator. Today, companies are building data pipelines and running them alongside their continuous integration/continuous delivery (CI/CD) systems. Data Science has moved out of research and into operations.
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