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- Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
- When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode 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!
- So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.
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- Your host as usual is Tobias Macey and today I’m interviewing Sam Willis about Tetra, a full stack component framework for your Django applications
- How did you get introduced to Python?
- Can you describe what Tetra is and the story behind it?
- What are the problems that you are aiming to solve with this project?
- What are some of the other ways that you have addressed those problems?
- What are the shortcomings that you encountered with those solutions?
- What was missing in the existing landscape of full-stack application development patterns that prompted you to build a new meta-framework?
- What are some of the sources of inspiration (positive and negative) that you looked to while deciding on the component selection and implementation strategy?
- Can you describe how Tetra is implemented?
- What are the core principles that you are relying on to drive your design of APIs and developer experience?
- What is the process for building a full component in Tetra?
- A perennial challenge with combining the syntax across multiple languages in a single file is editor support. How are you thinking about that with Tetra’s implementation?
- What is your grand vision for Tetra and how are you working to make it sustainable?
- What are the most interesting, innovative, or unexpected ways that you have seen Tetra used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Tetra?
- When is Tetra the wrong choice?
- What do you have planned for the future of Tetra?
Keep In Touch
- Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.
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- Tetra Framework
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