Safely Test Your Applications And Analytics With Production Quality Data Using Tonic AI
Data Engineering Podcast
00:00
Orms and ORMs for Machine Learning?
In terms of the core target use case that you're focusing on, you said that you're aiming at developers. But this obviously has some applicability to analytical use cases and essentially kind of machine learning use cases. I'm also curious, in your opinion, why tools like Django and Rails that have these built-in ORMs where it's incorporated into the application development experience are not available for fake data generation.
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Summary
The most interesting and challenging bugs always happen in production, but recreating them is a constant challenge due to differences in the data that you are working with. Building your own scripts to replicate data from production is time consuming and error-prone. Tonic is a platform designed to solve the problem of having reliable, production-like data available for developing and testing your software, analytics, and machine learning projects. In this episode Adam Kamor explores the factors that make this such a complex problem to solve, the approach that he and his team have taken to turn it into a reliable product, and how you can start using it to replace your own collection of scripts.
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 Adam Kamor about Tonic, a service for generating data sets that are safe for development, analytics, and machine learning
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Tonic is and the story behind it?
- What are the core problems that you are trying to solve?
- What are some of the ways that fake or obfuscated data is used in development and analytics workflows?
- challenges of reliably subsetting data
- impact of ORMs and bad habits developers get into with database modeling
- Can you describe how Tonic is implemented?
- What are the units of composition that you are building to allow for evolution and expansion of your product?
- How have the design and goals of the platform evolved since you started working on it?
- Can you describe some of the different workflows that customers build on top of your various tools
- What are the most interesting, innovative, or unexpected ways that you have seen Tonic used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Tonic?
- When is Tonic the wrong choice?
- What do you have planned for the future of Tonic?
Contact Info
- @AdamKamor on Twitter
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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- 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.
- To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers
Links
- Tonic
- Django
- Ruby on Rails
- C#
- Entity Framework
- PostgreSQL
- MySQL
- Oracle DB
- MongoDB
- Parquet
- Databricks
- Mockaroo
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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