We like to keep things real here at Snaplet. Meeauwmsayin!? So why use fake data when you can code against real production-like data? Before we get into the benefits of coding against it, let’s answer your question:
What is production-like data?
According to, well us here at Snaplet, production-like data is a specified sample of production (or any other source’s) data that has been automatically anonymized or de-identified. (Spoiler alert, This is exactly what Snaplet snapshot does). That means, the data you are working with, mimics the data in your production database but it contains no private stuff, like personally identifiable information. We prefer the acronym PII (-> more on PII).
But isn’t it just a fancy word for a data dump?
It is not. Think of it as a more superior form of postgres pg_dump. Because we automatically de-identify all sensitive data, it is the closest thing you will get to a synthesized representation of your production database but considerably smaller (to download) and a great deal safer (thanks to the PII removed).
What are the benefits of coding against production-like data?
Coding against production-like or production-realistic data, offers several significant benefits for developers. Here's a list of the key advantages:
- Realistic testing environment: Production-like data provides an environment that closely mirrors the actual production system. This allows for more accurate and realistic testing scenarios.
- Identifying real-world issues: Testing with production-like data can uncover real-world issues and edge cases that might not be apparent with fake or sample data.
- Improved quality assurance: It enhances your code quality by ensuring that applications are tested in conditions that replicate the production environment as closely as possible.
- Accurate performance testing: Developers can assess an application's performance, scalability, and resource utilization under conditions that are representative of the actual user load.
- Behaviour validation: Production-like data helps verify whether the application behaves correctly when interacting with the data that will be used in the real world.
- Data integrity checks: Testing against realistic data allows for data integrity checks, ensuring that data remains consistent and reliable throughout the application's lifecycle.
- Enhanced security testing: Developers can identify security vulnerabilities and potential breaches more effectively when testing with data that resembles the production environment.
- Streamlined debugging: Debugging and issue resolution become easier as developers work with data that closely resembles the data users will encounter.
- Compliance testing: Ensuring compliance with data protection regulations and privacy laws is more straightforward when testing with production-like data that includes sensitive information (PII).
- User experience validation: Developers can assess and improve the user experience by interacting with data that mimics real user behavior and preferences.
- Better training and onboarding: Production-realistic data can be used for training and onboarding purposes, helping new team members become familiar with the application's behavior and data interactions. Read more about how Snaplet significantly reduced onboarding time for TRUNK.
- Regression testing: It facilitates effective regression testing, allowing developers to detect and fix issues that may arise as the application evolves.
- Optimized queries and performance: Working with actual data helps in optimizing database queries and improving overall application performance.
- Realistic load testing: Load testing with production-like data ensures that the application can handle the anticipated user load without performance degradation.
- Business continuity: By testing against real data, organizations can prepare for real-world scenarios, ensuring business continuity in case of system failures or data losses.
- User feedback integration: With realistic data, user feedback and user behavior patterns can be better integrated into the development process.
- Cost efficiency: It can be cost-efficient as synthetic data generation for testing can be time-consuming and may not fully represent real-world scenarios.
- Improved predictive analysis: Developers can utilize production-like data for predictive analytics and machine learning models, leading to more accurate insights.
- Better user satisfaction: Ultimately, working with production-like data contributes to delivering a more reliable and user-friendly application, which leads to higher user satisfaction.
- Happy developers: Coding against production-like data eliminates the feeling of “coding blind”, because developers know and understand their data.
In summary, coding against production-like data offer numerous advantages for developers from improved debugging, to better security testing and enhanced user experience validation. And when devs are happy, companies are happy, since all these benefits contribute to the overall success and reliability of software applications.
Snaplet for easy data generation- and anonymization
Snaplet is a composable data generation- and database anonymization tool for software developers. This means there are many ways one can use it. You might want to start off with Snaplet seed and later need to use a snapshot. With Seed, you can populate your database with deterministic mock data, without you having to write any scripts. This is great when you quickly want to generate seed data in order to get a new project off the ground fast. It is also the quickest way to try out our product.
Snaplet snapshot copies a sample of your production database, automatically de-identifying sensitive information, making it safe and easy to code against production-like data and share this data with others. Snaplet puts the data straight into the hands of the developer without any restrictions or effort. If you want better data to code against, in other words, production-like data, without having to write seed scripts or resort to postgres dumps, then give Snaplet a try.
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