An Opinionated Look At End-to-end Code Only Analytical Workflows With Bruin
Data Engineering Podcast
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Summary
The challenges of integrating all of the tools in the modern data stack has led to a new generation of tools that focus on a fully integrated workflow. At the same time, there have been many approaches to how much of the workflow is driven by code vs. not. Burak Karakan is of the opinion that a fully integrated workflow that is driven entirely by code offers a beneficial and productive means of generating useful analytical outcomes. In this episode he shares how Bruin builds on those opinions and how you can use it to build your own analytics without having to cobble together a suite of tools with conflicting abstractions.
Announcements
Parting Question
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
The challenges of integrating all of the tools in the modern data stack has led to a new generation of tools that focus on a fully integrated workflow. At the same time, there have been many approaches to how much of the workflow is driven by code vs. not. Burak Karakan is of the opinion that a fully integrated workflow that is driven entirely by code offers a beneficial and productive means of generating useful analytical outcomes. In this episode he shares how Bruin builds on those opinions and how you can use it to build your own analytics without having to cobble together a suite of tools with conflicting abstractions.
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 Burak Karakan about the benefits of building code-only data systems
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Bruin is and the story behind it?
- Who is your target audience?
- There are numerous tools that address the ETL workflow for analytical data. What are the pain points that you are focused on for your target users?
- How does a code-only approach to data pipelines help in addressing the pain points of analytical workflows?
- How might it act as a limiting factor for organizational involvement?
- Can you describe how Bruin is designed?
- How have the design and scope of Bruin evolved since you first started working on it?
- You call out the ability to mix SQL and Python for transformation pipelines. What are the components that allow for that functionality?
- What are some of the ways that the combination of Python and SQL improves ergonomics of transformation workflows?
- What are the key features of Bruin that help to streamline the efforts of organizations building analytical systems?
- Can you describe the workflow of someone going from source data to warehouse and dashboard using Bruin and Ingestr?
- What are the opportunities for contributions to Bruin and Ingestr to expand their capabilities?
- What are the most interesting, innovative, or unexpected ways that you have seen Bruin and Ingestr used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Bruin?
- When is Bruin the wrong choice?
- What do you have planned for the future of Bruin?
Parting Question
- 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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
- 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 hosts@dataengineeringpodcast.com with your story.
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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