Synthetic Data Generation Using Mimesis with Nikita Sobolev
Summary
Most applications require data to operate on in order to function, but sometimes that data is hard to come by, so why not just make it up? Mimesis is a library for randomly generating data of different types, such as names, addresses, and credit card numbers, so that you can use it for testing, anonymizing real data, or for placeholders. This week Nikita Sobolev discusses how the project got started, the challenges that it has posed, and how you can use it in your applications.
Preface
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- Your host as usual is Tobias Macey and today I’m interviewing Nikita Sobolev about Mimesis, a library for quickly generating synthetic data
Interview
- Introductions
- How did you get introduced to Python?
- What is mimesis and how does it compare to other projects such as faker and factory_boy?
- What was the motivation for creating it?
- One of the features that is advertised is the speed of Mimesis. What techniques are used to ensure that the data is generated quickly?
- What are the built in mechanisms for generating data?
- What options do users have for customizing the types of data that can get generated?
- What are some of the most complicated providers to write and maintain?
- What are some of the use cases outside of unit or integration tests where Mimesis could be beneficial?
- How would you use Mimesis to anonymize data from a production environment to be used for testing?
- What are the most challenging aspects of maintaining the Mimesis project?
- What are some of the plans that you have for the future of Mimesis?
Keep In Touch
Picks
- Tobias
- Nikita
Links
- Mimesis
- Django
- Faker
- Factory Boy
- Internationalization (I18N)
- Unicode
- Enum
- Pipfile
- GeoJSON
- Mimesis Cloud
- Sanic
- GraphQL
- Impostor Syndrome
- Imposter Syndrome Disclaimer: Add this to all of your projects!
- Jacob Kaplan-Moss PyCon Keynote
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Activity