DataSunrise Achieves AWS DevOps Competency Status in AWS DevSecOps and Monitoring, Logging, Performance

Test Data Management

Test Data Management

Introduction

In today’s fast-paced software development world, quality assurance is crucial. One often overlooked aspect of this process is test data management. This article will explore the basics of such data, its importance, and how it can improve your testing processes.

What is Test Data Management?

Test data management is the process of creating, organizing, and maintaining data used in software testing. It ensures that test data is accurate, consistent, and available when needed. This practice is essential for effective quality assurance and reliable test results.

Why is Test Data Important?

  1. Improved test coverage
  2. Increased efficiency in testing processes
  3. Enhanced data security and compliance
  4. Better collaboration among team members

Types of Test Data

When managing test data, it’s important to consider various types:

Real Data

Real data comes from production environments. While it’s authentic, it may contain sensitive information and require careful handling.

Synthetic Data

Synthetic data is artificially created to mimic real data. It’s beneficial for:

  • Protecting sensitive information
  • Creating edge cases
  • Scaling test scenarios

Masked Data

Masked data is real data with sensitive information obscured. This approach maintains data relationships while protecting privacy.

DataSunrise offers excellent data masking capabilities. Make sure to understand these features, as they are a great way to create test data along with data generation.

Creating and Managing Test Data

Effective test data management involves several key steps:

1. Data Requirements Analysis

Identify the data needed for each test case. Consider:

  • Data types
  • Volume
  • Relationships between data points

DataSunrise offers flexible data generation capabilities. It allows you to specify the volume of data to be generated and lets you choose a specific generator type for each column. This level of customization ensures that your test data accurately reflects your requirements. For example, we have the following tables for our application:

This is a DBeaver ER diagram (in crow’s foot notation). Here is the code to create these tables:

-- Create the Cities table
CREATE TABLE Cities (
  CityID INT PRIMARY KEY,
  CityName VARCHAR(100) NOT NULL,
  Country VARCHAR(50) NOT NULL
);
-- Create the Personnel table
CREATE TABLE Personnel (
  EmployeeID INT PRIMARY KEY,
  FirstName VARCHAR(50) NOT NULL,
  LastName VARCHAR(50) NOT NULL,
  CityID INT,
  FOREIGN KEY (CityID) REFERENCES Cities(CityID)
);
-- Insert sample data into Cities table
INSERT INTO Cities (CityID, CityName, Country) VALUES
(1, 'New York City', 'USA'),
(2, 'London', 'UK'),
(3, 'Tokyo', 'Japan');
-- Insert sample data into Personnel table
INSERT INTO Personnel (EmployeeID, FirstName, LastName, CityID) VALUES
(1, 'John', 'Doe', 1),
(2, 'Jane', 'Smith', 2);

2. Data Generation

Create test data that meets your requirements. This may involve:

  • Extracting and masking production data
  • Using synthetic data generation tools
  • Manually creating test data sets

In DataSunrise we create an Instance and create a Periodic Task having the type of ‘Synthetic Data Generation’. The system automatically tracks the foreign key relation. There is a nuance to work with it. Primary keys and foreign keys may be difficult to track.

And for performance reasons there is an option of truncating the table. You can check the corresponding box during the task creation. Here you can see the tables selected for data generation:

Here is a generated data (DBeaver table preview for both tables):

3. Data Storage and Version Control

Properly store and version your test data. This ensures:

  • Easy access for testers
  • Traceability of changes
  • Consistency across test environments

4. Data Refresh and Maintenance

Regularly update your test data to:

  • Reflect changes in production environments
  • Remove outdated or irrelevant data
  • Maintain data quality and relevance

DataSunrise allows you to monitor all periodic data generation tasks efficiently. Complicated tables can cause mistakes, so it’s helpful to have detailed reports for data tasks. Here’s an example of such a report:

Examples of Test Data Management in Action

Let’s look at some practical examples of test data management:

Example 1: E-commerce Website Testing

Scenario: Testing a new feature for product recommendations

Test data required:

  • User profiles (age, gender, location)
  • Purchase history
  • Product catalog

Setup:

  1. Create a test database with tables for users, products, and orders
  2. Generate synthetic user profiles using a data generation tool
  3. Create sample purchase histories based on user profiles
  4. Import a subset of the actual product catalog

Result: With this test data, you can effectively test the recommendation algorithm’s performance across various user segments and product categories.

Example 2: Banking Application Security Testing

Scenario: Testing access controls and data encryption

Test data required:

  • User accounts with various permission levels
  • Sample financial transactions
  • Sensitive customer information (masked)

Setup:

  1. Create a test environment mirroring the production database structure
  2. Generate synthetic user accounts with different roles (admin, teller, customer)
  3. Create sample transactions using a data generation tool
  4. Import masked customer data from production

Result: This test data allows you to verify access controls, encryption processes, and audit trails without risking real customer information.

Managing Data Relationships in Test Data

Maintaining data relationships is crucial for accurate testing. Here are some tips:

  1. Use referential integrity constraints in your test databases
  2. Ensure foreign key relationships are preserved when generating synthetic data
  3. Create data generation scripts that maintain logical connections between entities

Example: When testing an order processing system, ensure that:

  • Each order is associated with a valid customer
  • Product quantities in orders don’t exceed available inventory
  • Order dates align with customer registration dates

Best Practices for Test Data Management

To optimize your test data management process:

  1. Automate data generation and refresh processes
  2. Implement robust version control for test data sets
  3. Use data virtualization to reduce storage needs
  4. Regularly audit and clean up test data
  5. Ensure compliance with data protection regulations
  6. Document your test data management processes

Challenges in Test Data Management

While essential, test data management comes with its challenges:

  1. Balancing data volume with storage limitations
  2. Ensuring data privacy and compliance
  3. Maintaining data consistency across environments
  4. Keeping test data up-to-date with production changes

Addressing these challenges requires a combination of tools, processes, and organizational commitment.

The Future of Test Data Management

As software systems grow more complex, test data management will continue to evolve. Emerging trends include:

  1. AI-driven test data generation
  2. Cloud-based test data management solutions
  3. Integration with continuous testing pipelines
  4. Advanced data masking and synthetic data techniques

Staying informed about these trends can help organizations stay ahead in their quality assurance efforts.

Conclusion

Test data management is a critical component of effective software testing and quality assurance. By implementing robust test data practices, organizations can improve test coverage, increase efficiency, and ensure the reliability of their software products.

From creating synthetic data to managing complex data relationships, effective test data management requires careful planning and execution. Follow the tips in this article to improve your testing and deliver better software.

DataSunrise offers user-friendly tools for database security and test data management. These tools include a synthetic data generation tool. They are designed to simplify processes for users.

Visit the DataSunrise website to see a demo of our solutions. Our solutions can enhance your test data management and database security.

Next

Static Data Masking

Static Data Masking

Learn More

Need Our Support Team Help?

Our experts will be glad to answer your questions.

Countryx
United States
United Kingdom
France
Germany
Australia
Afghanistan
Islands
Albania
Algeria
American Samoa
Andorra
Angola
Anguilla
Antarctica
Antigua and Barbuda
Argentina
Armenia
Aruba
Austria
Azerbaijan
Bahamas
Bahrain
Bangladesh
Barbados
Belarus
Belgium
Belize
Benin
Bermuda
Bhutan
Bolivia
Bosnia and Herzegovina
Botswana
Bouvet
Brazil
British Indian Ocean Territory
Brunei Darussalam
Bulgaria
Burkina Faso
Burundi
Cambodia
Cameroon
Canada
Cape Verde
Cayman Islands
Central African Republic
Chad
Chile
China
Christmas Island
Cocos (Keeling) Islands
Colombia
Comoros
Congo, Republic of the
Congo, The Democratic Republic of the
Cook Islands
Costa Rica
Cote D'Ivoire
Croatia
Cuba
Cyprus
Czech Republic
Denmark
Djibouti
Dominica
Dominican Republic
Ecuador
Egypt
El Salvador
Equatorial Guinea
Eritrea
Estonia
Ethiopia
Falkland Islands (Malvinas)
Faroe Islands
Fiji
Finland
French Guiana
French Polynesia
French Southern Territories
Gabon
Gambia
Georgia
Ghana
Gibraltar
Greece
Greenland
Grenada
Guadeloupe
Guam
Guatemala
Guernsey
Guinea
Guinea-Bissau
Guyana
Haiti
Heard Island and Mcdonald Islands
Holy See (Vatican City State)
Honduras
Hong Kong
Hungary
Iceland
India
Indonesia
Iran, Islamic Republic Of
Iraq
Ireland
Isle of Man
Israel
Italy
Jamaica
Japan
Jersey
Jordan
Kazakhstan
Kenya
Kiribati
Korea, Democratic People's Republic of
Korea, Republic of
Kuwait
Kyrgyzstan
Lao People's Democratic Republic
Latvia
Lebanon
Lesotho
Liberia
Libyan Arab Jamahiriya
Liechtenstein
Lithuania
Luxembourg
Macao
Madagascar
Malawi
Malaysia
Maldives
Mali
Malta
Marshall Islands
Martinique
Mauritania
Mauritius
Mayotte
Mexico
Micronesia, Federated States of
Moldova, Republic of
Monaco
Mongolia
Montserrat
Morocco
Mozambique
Myanmar
Namibia
Nauru
Nepal
Netherlands
Netherlands Antilles
New Caledonia
New Zealand
Nicaragua
Niger
Nigeria
Niue
Norfolk Island
North Macedonia, Republic of
Northern Mariana Islands
Norway
Oman
Pakistan
Palau
Palestinian Territory, Occupied
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Pitcairn
Poland
Portugal
Puerto Rico
Qatar
Reunion
Romania
Russian Federation
Rwanda
Saint Helena
Saint Kitts and Nevis
Saint Lucia
Saint Pierre and Miquelon
Saint Vincent and the Grenadines
Samoa
San Marino
Sao Tome and Principe
Saudi Arabia
Senegal
Serbia and Montenegro
Seychelles
Sierra Leone
Singapore
Slovakia
Slovenia
Solomon Islands
Somalia
South Africa
South Georgia and the South Sandwich Islands
Spain
Sri Lanka
Sudan
Suriname
Svalbard and Jan Mayen
Swaziland
Sweden
Switzerland
Syrian Arab Republic
Taiwan, Province of China
Tajikistan
Tanzania, United Republic of
Thailand
Timor-Leste
Togo
Tokelau
Tonga
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Turks and Caicos Islands
Tuvalu
Uganda
Ukraine
United Arab Emirates
United States Minor Outlying Islands
Uruguay
Uzbekistan
Vanuatu
Venezuela
Viet Nam
Virgin Islands, British
Virgin Islands, U.S.
Wallis and Futuna
Western Sahara
Yemen
Zambia
Zimbabwe
Choose a topicx
General Information
Sales
Customer Service and Technical Support
Partnership and Alliance Inquiries
General information:
info@datasunrise.com
Customer Service and Technical Support:
support.datasunrise.com
Partnership and Alliance Inquiries:
partner@datasunrise.com