In the world of database management, one often encounters the term "seed data." Although it is borrowed from agriculture, it is not actually related to pots, plants or soil. That being said, you may still encounter a bug or two. Let’s dig into the concept of seed data to unearth its definition, purpose, and its crucial role in shaping the data landscape within a database.
What is seed data?
Seed data refers to the initial set of data that is loaded into a database when it is first created or when an application is first installed. It serves as the starting point for the database, providing the essential information necessary for the system to function correctly.
How do I get seed data for local development and tests?
The process of creating seed data typically involves writing queries to populate your database with the data you need. This can be done using SQL scripts, database migration tools, making a SQL dump, using dummy data libraries like Faker.js or using a generative ai tool like Snaplet seed to automatically seed your testing database with synthesized production-like data.
Why do I need to create seed data?
Seed data is instrumental in initializing a database to a usable state. When a new database is created, it needs to have certain essential data preloaded to support the application's basic functionalities. This may include default user roles, system settings, or other foundational elements. The data that is entered should be realistic, relevant, and reflect production data to ensure the proper functioning of the application. Environment parity is crucial in maintaining consistency across all your environments (development, testing, and production). Seed data plays a pivotal role in ensuring that these environments start with the same baseline, reducing the chances of errors or discrepancies when the application is deployed across different stages of development. This parity ensures that each environment closely mirrors the others, allowing for smoother transitions and more reliable testing, contributing to the overall stability of the application throughout its lifecycle.
Why do I need to create production-like seed data? Why not just use Lorem ipsum?
You know what they say: garbage in, garbage out. Starting off right just sets you up for better success. Just like won’t plant your agricultural seed in concrete, you don’t want to create inefficient data to seed your database. A good set of seed data should include default values and configurations that are necessary for the application to run smoothly. Although Lorem ipsum might work in some cases, in these instances, just random fake data will lead to bugs and tests that fail.
What else can I use seed data for?
Seed data is great for demonstrations and training purposes. It allows you to interact with the system using a standardized set of data, making it easier to showcase features, conduct training sessions, and simulate real-world scenarios without compromising privacy. It can also be used when onboarding new team members. Seed data is also used in unit testing and end-to-end testing. But for tests to work, or work as they should (see this post), your seed data should be realistic and relevant.
What are considered best practices for managing seed data?
1. Version control
Just like application code, seed data should be version-controlled to track changes and ensure that different environments are synchronized.
2. Separation from test data
Although it is important to maintain consistency across all your environments it is important to remember that test data is used for specific testing scenarios and should be kept separate to maintain clarity. In other words, you will probably need to create a different set of seed data for different scenarios.
3. Documentation
Documenting seed data is crucial for developers, administrators, and anyone involved in managing the database. Clear documentation ensures that everyone understands the purpose and structure of the seed data.
It is easy to see how database management can become a real pain. Manually creating and updating scripts as well as their accompanying documentation, are tedious. Keeping track of which datasets to use where can be hard to maintain. If you’re doing it according to best practice it’s painful and when you get it right, all that happens is nothing goes wrong unexpectedly. The whole exercise feels bothersome and like a hygiene exercise, and to be frank, it is. Solving problems and building products are what attracts most developers. Sometimes it just seems easier to create a plain old database dump or just start typing garbage data into your database to get started. We’ve all been there and done that. Sometimes we get away with it, but at other times it can have unwanted (and unnecessary) consequences. This is exactly why we created Snaplet seed.
Using Snaplet seed for automated database seeding
Snaplet seed leverages generative AI to make it fast and easy to seed your development and testing databases with production-like data. This significantly accelerates and simplifies the database seeding process, which means you can spend more time doing work you actually like.
Generative AI for realistic data
Snaplet seed understands and replicates the structure of your database. This ensures that the seeded data closely mimics real-world scenarios, capturing the nuances and characteristics of actual production data.
Considering database intricacies
Snaplet seed goes beyond simple data generation by considering relationships between tables, constraints, and other intricacies within your database. The result is not just realistic data but also meaningful information tailored to the context of your application.
Customizable seed profiles
Although we try our best, we don’t always get the data right. That is why we give the ability to customize your data to specific scenarios.
TypeScript interface for data generation
The TypeScript interface adds a layer of versatility to the seeding process. This means that you can make changes to the seed in a language you know and understand and seamlessly integrate Snaplet seed into your existing workflow and coding practices.
Allows for different testing scenarios
Snaplet seed is great for unit testing as well as E2E (end-to-end) testing. You can effortlessly create diverse and realistic datasets specifically tailored to mimic real-world scenarios. This capability enhances the efficiency and accuracy of end-to-end testing processes, ensuring that your application is thoroughly validated across its entire workflow.
Determinism and consistency
Snaplet seed's deterministic data generation is particularly beneficial for testing environments. The consistency in data outputs reduce bugs and ensures that tests are reliable and reproducible, contributing to the overall robustness of the application.
In the fields of database management, Snaplet seed provides a much faster alternative to generating realistic, production-like seed data. It is an important ingredient in the entire development workflow, from development to testing, as it mirrors the intricate relationships within a fertile ecosystem. By reducing assumptions, bias and bugs, snaplet ‘seed’ becomes your reliable ally in producing and harvesting quality code.
Snaplet seed is currently available an experimental preview feature. Check out our docs to get started.
Happy coding!
