Data Engineering Podcast

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

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02 June 2021

Making Data Pipelines Self-Serve For Everyone With Shipyard - E191

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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.


  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
  • RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at today.
  • When it comes to serving data for AI and ML projects, do you feel like you have to rebuild the plane while you’re flying it across the ocean? Molecula is an enterprise feature store that operationalizes advanced analytics and AI in a format designed for massive machine-scale projects without having to manage endless one-off information requests. With Molecula, data engineers manage one single feature store that serves the entire organization with millisecond query performance whether in the cloud or at your data center. And since it is implemented as an overlay, Molecula doesn’t disrupt legacy systems. High-growth startups use Molecula’s feature store because of its unprecedented speed, cost savings, and simplified access to all enterprise data. From feature extraction to model training to production, the Molecula feature store provides continuously updated feature access, reuse, and sharing without the need to pre-process data. If you need to deliver unprecedented speed, cost savings, and simplified access to large scale, real-time data, visit and request a demo. Mention that you’re a Data Engineering Podcast listener, and they’ll send you a free t-shirt.
  • 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


  • 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?

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

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