Building an end-to-end data pipeline for your machine learning projects is a complex task, made more difficult by the variety of ways that you can structure it. Kedro is a framework that provides an opinionated workflow that lets you focus on the parts that matter, so that you don’t waste time on gluing the steps together. In this episode Tom Goldenberg explains how it works, how it is being used at Quantum Black for customer projects, and how it can help you structure your own. Definitely worth a listen to gain more understanding of the benefits that a standardized process can provide.
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- Your host is Tobias Macey and today I’m interviewing Tom Goldenberg about Kedro, an open source development workflow tool that helps structure reproducible, scaleable, deployable, robust and versioned data pipelines.
- How did you get involved in the area of data management?
- Can you start by explaining what Kedro is and its origin story?
- Who are the primary users of Kedro, and how does it fit into and impact the workflow of data engineers and data scientists?
- Can you talk through a typical lifecycle for a project that is built using Kedro?
- What are the overall features of Kedro and how do they compound to encourage best practices for data projects?
- How does the culture and background of QuantumBlack influence the design and capabilities of Kedro?
- What was the motivation for releasing it publicly as an open source framework?
- What are some examples of ways that Kedro is being used within QuantumBlack and how has that experience informed the design and direction of the project?
- Can you describe how Kedro itself is implemented and how it has evolved since you first started working on it?
- There has been a recent trend away from end-to-end ETL frameworks and toward a decoupled model that focuses on a programming target with pluggable execution. What are the industry pressures that are driving that shift and what are your thoughts on how that will manifest in the long term?
- How do the capabilities and focus of Kedro compare to similar projects such as Prefect and Dagster?
- It has not yet reached a stable release. What are the aspects of Kedro that are still in flux and where are the changes most concentrated?
- What is still missing for a stable 1.x release?
- What are some of the most interesting/innovative/unexpected ways that you have seen Kedro used?
- When is Kedro the wrong choice?
- What do you have in store for the future of Kedro?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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