This image represents these themes: Local Development, Seeding Databases, Production Data, Data Realism, Snaplet, GDPR Compliance, Database Snapshots, Local Environment, Developer Collaboration, Seed Scripts, Synthetic Data, Manual Data Insertion

How to seed your local development database

Snaplet versus seed scripts, database dumps or synthetic data

One of the critical aspects of local development is populating your database with data that mirrors your production environment. This is essential for effectively simulating real-world conditions for both development and testing. Over the years, several approaches have emerged to getting production data into your local environment. However, each method has its shortcomings, such as the risk of exposing sensitive data or not adequately representing production conditions. Let’s explore the techniques for seeding your local development database and then contrast them to Snaplet.

Traditional methods of seeding local databases

Manually inserting data

  • Pros: Direct control over data.
  • Cons: Time-consuming, error-prone, and doesn't accurately reflect production.

Manually inserting data into your local database gives you a high degree of control, allowing you to input specific records that test edge cases or problematic scenarios. However, this method can be extremely time-consuming and error-prone. As your database schema evolves, you may need to continuously update the manually entered data to keep it in sync with the production schema. Additionally, manually inserted data may lack the complexity and diversity of real-world data, making it less suitable for thorough testing. It’s also painfully tedious, and on the whole, very fragile. In reality, most developers who insert data manually into their environment are probably doing some version of mock data anyway.

Production dumps

  • Pros: Realistic data.
  • Cons: Risk of exposing sensitive information, impracticality due to scale, and GDPR compliance issues.

Using production dumps, such as PostgreSQL's pg_dump, is one of the most accurate ways to populate your local environment with data that reflects your actual production database. This ensures a perfect level of data realism but comes with significant drawbacks.

Firstly, using production data locally can pose severe security and regulatory risks, especially if the data contains sensitive or personally identifiable information (PII) which is subject to GDPR and other data privacy restrictions.

Secondly, production databases can be large, and it may not be practical to import the entire dataset into your local environment, and running a dump may negatively impact the performance of the database.

Lastly, access to production data is typically restricted (and for good reason), meaning this is not a solution that’s broadly available to all the members of a software development team.

Seed scripts

  • Pros: Automates the data population process.
  • Cons: Requires maintenance, and data may not be reflective of real-world scenarios.

Seed scripts automate the process of populating a database with an initial set of data. They can be customized to produce data that's useful for your specific application, and they run quickly.

However, the data is synthetic and might not capture all the nuances of production data. Over time, you may find that the seed scripts need to be updated frequently to align with changes to the database schema or to introduce new kinds of test data. This makes them inherently fragile, and they require constant maintenance. Over time, understanding the nuances of how they behave can make them very difficult to maintain.

Synthetic data

  • Pros: Can produce large datasets.
  • Cons: Often expensive, lacks realism, and generated data may not adhere to business logic.

Using a synthetic data generator can provide you with large volumes of data quickly. These generators create data that conforms to your database schema and can even simulate relationships between tables. However, the data is entirely fictitious and may not accurately reflect the types of data issues—like missing values or inconsistent formatting—that you'll encounter in a production environment. Because of the complexity involved, these solutions are often pricy to implement and maintain.

The Snaplet solution

Snaplet offers a unique approach to local database seeding, addressing many of the challenges posed by traditional methods. Here's how Snaplet compares:

Data realism

Snaplet creates snapshots of your production data that can be used to populate your local database, ensuring a high degree of realism. This is invaluable for developers who want to simulate real-world conditions without the manual labor and inaccuracies associated with seeding databases traditionally.

Security and compliance

One of the major concerns with directly importing data from production is the risk of exposing sensitive information. Snaplet addresses this by scrambling data from production while keeping the “shape” of the data consistent, ensuring that you're not only secure but also in compliance with regulations such as GDPR. Because the data’s shape remains the same, application and testing logic is not affected.


Snaplet facilitates team collaboration by allowing developers to share snapshots, ensuring everyone is on the same page. This feature is a significant improvement over traditional methods, where differing local environments can lead to the notorious "it works on my machine" syndrome.


Seeding your local development database is an essential but often overlooked aspect of the development lifecycle. While traditional methods like manual data insertion, production dumps, seed scripts, and synthetic data have their merits, they also come with significant drawbacks.

Snaplet offers an all-in-one solution that not only overcomes these challenges but also adds additional layers of security and collaboration, making it an indispensable tool for modern development teams.

If you're a developer looking for an efficient, secure, and realistic way to seed your local database, it might be time to give Snaplet a try.

Jian Reis
May 3, 2024