June 25, 2019

The Workflow Engine For Data Engineers And Data Scientists

1 hour 8 minutes

Summary

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.

Announcements

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Your host is Tobias Macey and today I’m interviewing Jeremiah Lowin about Prefect, a workflow platform for data engineering

Interview

Introduction

How did you get involved in the area of data management?

Can you start by explaining what Prefect is and your motivation for creating it?

What are the axes along which a workflow engine can differentiate itself, and which of those have you focused on for Prefect?

In some of your blog posts and your PyData presentation you discuss the concept of negative vs. positive engineering. Can you briefly outline what you mean by that and the ways that Prefect handles the negative cases for you?

How is Prefect itself implemented and what tools or systems have you relied on most heavily for inspiration?

How do you manage passing data between stages in a pipeline when they are running across distributed nodes?

What was your decision making process when deciding to use Dask as your supported execution engine?

For tasks that require specific resources or dependencies how do you approach the idea of task affinity?

Does Prefect support managing tasks that bridge network boundaries?

What are some of the features or capabilities of Prefect that are misunderstood or overlooked by users which you think should be exercised more often?

What are the limitations of the open source core as compared to the cloud offering that you are building?

What were your assumptions going into this project and how have they been challenged or updated as you dug deeper into the problem domain and received feedback from users?

What are some of the most interesting/innovative/unexpected ways that you have seen Prefect used?

When is Prefect the wrong choice?

In your experience working on Airflow and Prefect, what are some of the common challenges and anti-patterns that arise in data engineering projects?

What are some best practices and industry trends that you are most excited by?

What do you have planned for the future of the Prefect project and company?

Contact Info

@jlowin on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Prefect

Airflow

Dask

Podcast Episode

Prefect Blog

PyData Presentation

Tensorflow

Workflow Engine

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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By Tobias Macey

4.6

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