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Data Sourcing Roller Coaster

Programming Testing

So, you have built an app, and it’s time to see how it handles mass data. However, you must populate your database with valid test data to create realistic test scenarios. But where to find actual data and how to source it?

To make your job easier, we’ll go through the latest trends in data sourcing. Such data sets are crucial for practical testing because data quality can often affect the reliability of your applications. And, we all know that in a technology-driven world, software performance is a critical factor for success.

Fake It ’til You Make It - Synthetic Data Generation #

One of the latest trends in data sourcing is synthetic data generation. Artificially created synthetic data will mimic the characteristics of real-world data. This method is highly effective for populating your database as it successfully tackles several challenges:

  • Data Privacy: With strict data privacy regulations like GDPR and CCPA, using actual user data for testing can be risky. Synthetic data provides a privacy-compliant alternative.

  • Data Diversity: Synthetic data allows you to create diverse data sets, ensuring that your testing scenarios cover many possibilities.

  • Data Volume: Generating large volumes of realistic data for testing purposes can be challenging with real data. Synthetic data generation tools make it easier to create substantial data sets.

An array of tools and libraries available today (such as Faker, PySynthetic, and TensorFlow Data Validation) can assist you in generating synthetic data tailored to your specific testing needs.

Shh… It’s a Secret - Data Masking and Anonymization #

Another way to gather test data is to utilize information from your production environment. So, instead of fabricating everything from scratch, you’re using existing data sets. However, it’s crucial to ensure that this data doesn’t contain any confidential details about your users, such as their names and addresses.

Remember, protecting data privacy is of utmost importance, and it leads us to another significant trend: the adoption of data masking and anonymization techniques. As concerns about data breaches and privacy violations continue growing, organizations use these methods to safeguard sensitive information when conducting tests.

  • Data Masking involves replacing sensitive data with fictional or scrambled data that maintains its format but hides its true values. This method ensures the hiding of sensitive information in testing environments.

  • Data Anonymization: Anonymization goes a step further by modifying data to make it impossible to identify individuals. Hiding an individual’s identity is especially crucial when dealing with healthcare or financial data.

Leading database systems now offer built-in data masking and anonymization features. Also, third-party solutions like Delphix and Informatica specialize in data privacy protection and can help you mask your data and anonymize it.

Data from the Wild - Open Data Sets and Public APIs #

How can you acquire realistic data when you’re at the initial stages of a project and lack production data? The solution lies in making use of open data sets and public APIs.

The availability of open data sets and public APIs has transformed the way we gather data for testing. They provide a vast source of real-world data that can be extremely valuable in creating thorough test scenarios.

  • Open Data Sets: Organizations and governments worldwide increasingly share data sets on various topics, from weather data to transportation statistics. We can leverage these to create realistic test data.

  • Public APIs: Many online platforms provide public APIs that allow you to access and integrate real data into your testing environments. For example, social media platforms, financial institutions, and mapping services often offer public APIs.

Integrating open data sets and public APIs into your testing processes can enhance the authenticity of your tests and help you identify issues that might not surface with synthetic data alone.

Keep It Organized - Data Versioning and Management #

Now, with so many data stacks, you can easily make a mess out of your testing. As data sourcing becomes more complex, it’s crucial to emphasize the importance of proper data versioning and management. Organizations, no matter the size, must implement a system for managing, tracking changes and maintaining the consistency of their test data sets.

  • Version Control: To streamline your processes, consider implementing version control systems similar to those used in software development. These systems allow you to track changes, collaborate efficiently, and revert to previous versions if necessary.

  • Data Catalogs: Create and maintain a catalog of available test data sets. This catalog should include descriptions, metadata, and usage guidelines. Having such data sets readily available makes it simpler for your team to find and utilize the appropriate data for their testing requirements.

Ensuring adequate data versioning and management is crucial for maintaining data integrity and guaranteeing that your tests use the most accurate and current data.

So, what’s the best way to survive the data-sourcing rollercoaster?

First and foremost, it’s good to follow trends and know what options you have out there. So, ensure you’re informed about the latest news in data sourcing. Currently, synthetic data generation, data masking, open data sets, and effective data versioning are all cutting-edge factors driving the evolution of data sourcing for testing. Integrating these trends into your testing procedures can provide you with a competitive advantage by ensuring the reliability and security of your software.

As the software development landscape continues to change, so will the sourcing and managing test data strategies. The sooner you embrace current trends, the easier it will be for you to survive this crazy rollercoaster ride in data sourcing theme park.