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HomeData Engineering and Data WarehousingMaking Data Pipelines Self-Serve For Everyone With Shipyard

Making Data Pipelines Self-Serve For Everyone With Shipyard

Summary

Every part of the business relies on data, yet only a small team has the context and expertise to build and maintain workflows and data pipelines to transform, clean, and integrate it. In order for the true value of your data to be realized without burning out your engineers you need a way for everyone to get access to the information they care about. To help make that a more tractable problem Blake Burch co-founded Shipyard. In this episode he explains the utility of a low code solution that lets non engineers create their own self-serve pipelines, how the Shipyard platform is designed to make that possible, and how it allows engineers to create reusable tasks to satisfy the specific needs of the business. This is an interesting conversation about how to make data more accessible and more useful by improving the user experience of the tools that we create.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I’m interviewing Blake Burch about Shipyard, and his mission to create the easiest way for data teams to launch, monitor, and share resilient pipelines with less engineering

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what you are building at Shipyard and the story behind it?
What are the main goals that you have for Shipyard?
How does it compare to other data orchestration frameworks in the market?

Who are the target users of Shipyard and how does that influence the features and design of the product?
What are your thoughts on the role of data orchestration in the business?

How is the Shipyard platform implemented?
What was your process for identifying the core requirements of the platform?
How have the design and goals of the system evolved since you first began working on it?

Can you describe the workflow of building a data workflow with Shipyard?
How do you manage the dependency chain across tasks in the execution graph? (e.g. task-based, data assets, etc.)

How do you handle testing and data quality management in your workflows?
What is the interface for creating custom task definitions?
How do you address dependencies and sandboxing for custom code?

What is your approach to developing templates?
What are the operational challenges that you have had to address to manage scaling and multi-tenancy in your platform?
What are the most interesting, innovative, or unexpected ways that you have seen Shipyard used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Shipyard?
When is Shipyard the wrong choice?
What do you have planned for the future of Shipyard?

Contact Info

LinkedIn
@BlakeBurch_ on Twitter
Website
blakeburch on GitHub

Parting Question

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

Closing Announcements

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.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Shipyard
Zapier
Airtable
BigQuery
Snowflake
Podcast Episode

Docker
ECS == Elastic Container Service
Great Expectations
Podcast Episode

Monte Carlo
Podcast Episode

Soda Data
Podcast Episode

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

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