Is Database Subsetting Enough? How to Avoid Test Data Risks and Slowdowns

Many organizations turn to database subsetting for various reasons. For one, cloning entire terabyte datasets could bankrupt your cloud budget. And masked data could leave your teams fumbling with unrealistic test scenarios. Why wouldn't you just grab the data you need? 

Sometimes, it really is that straightforward. For certain use cases — like lightweight testing scenarios, proof-of-concepts, or applications with simple data structures — subsetting delivers exactly what it promises.

But for complex, interconnected systems? What looks like a clean solution quickly becomes complicated. 

Let's examine when database subsetting works, when it doesn't, and how to choose between traditional subsetting and a more modern solution.

What is Database Subsetting?

Database subsetting in test data management involves creating smaller, targeted datasets from larger production databases. Instead of using complete production data, teams can pull out specific parts needed for testing or development work. 

It is important to note that subsetting is different from synthetic data generation, static data masking, and dynamic data masking. It's common for organizations to use database subsetting for some use cases, in complement to other data masking methods and techniques.

Common Use Cases for Database Subsetting in Test Data Management

When Database Subsetting Makes Sense

Subsetting can be the right choice for your team if you have:

• Apps that need specific data patterns: Some applications depend on real data relationships. Examples include demographic analysis tools or pattern recognition systems. Another example is time-sensitive analytics that require specific seasonal effects or sales patterns during holiday seasons.
• New applications or features: Teams have clear data needs and only want specific subsets to test functionality.
• Limited resources: Full datasets are too big or expensive to copy. This happens often in on-premises, cloud, and SaaS environments.

The Drawbacks of Database Subsetting

But despite its benefits, organizations often face unexpected challenges.

Data Privacy and Compliance Risks

There’s a misconception that smaller datasets mean lower compliance risk. But this isn’t true. Subsetting does not inherently protect sensitive data, and it falls short of other measures, like data masking. Even small subsets can contain Social Security numbers, credit card details, or other personal information. 

The subset may be smaller, but compliance rules stay the same. If sensitive data ends up in the subset, you’re at risk of non-compliance with data privacy regulations like GDPR, HIPAA, and PCI DSS. 

From a data management perspective, subsetting can lead to data sprawl if not managed properly. Without central governance, an organization could end up with several fragmented subset instances of the source data.

Manual, Time-Intensive Processes

Many teams turn to subsetting in an effort to speed up testing. But it can have the opposite effect because of how long it can take.

Why? Subsetting requires a deep understanding of how data is interconnected. Many organizations may not realize this complexity. 

For example, there may be a simple task of choosing 10 customer records. But you might need to bring in data from 50 related tables with over 1,000 records because of the data’s foreign relationships that affect the usability of the resulting dataset.

Foreign key relationships are complex. Each record might have multiple parents, which creates a branching network that quickly grows beyond what teams expect. I've worked with organizations who initially estimated a simple customer subset would take a few days, only to discover weeks later that they were still untangling foreign key relationships across dozens of interconnected tables.

Additionally, on certain platforms, teams must also handle data insertion in the right order. The parent records must be inserted first before the child records and they must work down the relationship tree in a specific order.

Complex Dependency Resolution

Without the right tools, pulling a subset of data is tricky. It's difficult to filter a large dataset in a way that preserves table relationships — and you need to keep table relationships intact to avoid circular dependencies. 

The challenge grows when data is spread across multiple databases or platforms — a common situation in real-world applications.

Special Skills Required

Database subsetting isn't a one-size-fits-all operation. Oracle databases require different approaches than PostgreSQL, MySQL, or cloud-native platforms like Amazon RDS. Each database type demands specialized expertise to handle its unique constraints, relationships, and performance characteristics.

Successful subsetting typically requires two skills:

• Functional data knowledge that spans across multiple databases and platforms.
• Database administration expertise.

Most organizations may expect a basic subsetting to be completed within 1-2 weeks. But timelines depend on how nuanced the requirements are, such as if the need for referential integrity is well defined or the data spans across multiple databases.

In my experience, I've seen teams who thought they had the right expertise struggle with cross-database relationships. It turned what should have been a straightforward subsetting project into a months-long effort that required bringing in specialized consultants.

And without a tool, it becomes very difficult to make this repeatable, auditable, and consistent.

Missed Edge Cases and Outliers

The biggest danger of subsetting is missing edge cases. Smaller datasets do not capture these unusual scenarios. This makes subsetting unsuitable for automated testing or broad DevOps testing.

I've witnessed organizations discover critical bugs in production that their subset-based testing completely missed — bugs that would have been caught with full dataset testing.

When applications hit real-world scenarios that are too rare for the subset, testing misses potential problems. This is why you should only use subsetting for targeted development work.

Data Distribution Challenges

Keeping statistical patterns also makes subsetting more complex. Teams must preserve real patterns like demographics, geographic spread, and many other factors.

Application processes can also range from simple selection to complex AI-assisted matching. The risk of losing critical data patterns grows along with the complexity of the application.

Biased Data Selection

Subsetting enables organizations to be selective about their test and development datasets. But if the dataset is not selected with utmost care, it may not be representative of real-world data patterns — which can lead to wrong conclusions.

Broken Relationships

One of the main risks of subsetting is breaking referential integrity. If the subset extraction process does not honor the foreign key relationships across tables (or even schemas), the resulting subset may become unusable for testing and development.

When Subsetting Isn’t Enough

Database subsetting does not address data and privacy compliance risks. It also involves manual processes and can lead to missed edge cases and data distribution challenges.

So, what should you do? Adopt a more modern solution — one that combines virtualization and masking. Find out how this approach accelerates development, increases quality, ensures compliance, and reduces costs. And get four actionable checklists that will help you evaluate test data at your organization.

Get Your Test Data Strategy Guide

How Modern Solutions Solve Subsetting Challenges

Making the Right Choice: Database Subsetting vs. Perforce Delphix

Moving Beyond Database Subsetting: Next-Generation Test Data Management from Delphix

Database subsetting may seem cost-effective and effective at first. But it often creates:

• Serious compliance risks
• Operational slowdowns and inefficiencies
• Scale challenges
• Hidden costs

 These problems include:

• Missed edge cases in testing.
• False sense of security around sensitive data.
• Complex manual processes that take weeks of specialized work.

Delphix eliminates these tradeoffs by addressing the root causes behind why organizations consider subsetting in the first place.

Related blog >> What Is Delphix?

Integrate Data Masking with Test Data Delivery

With Delphix, teams experience 58% faster time to develop an application, and 77% more data and data environments are masked and protected.* 

The Delphix DevOps Data Platform addresses storage costs, compliance risks, and data volume management challenges by integrating data compliance and data virtualization on a single platform.

Delphix can discover sensitive data within an organization by scanning across databases, file systems, and platforms. Once discovered, Delphix can mask the sensitive data into realistic but fictitious equivalents — protecting against data breaches while ensuring compliance with privacy regulations like GDPR, HIPAA, and PCI DSS. 

By eliminating the challenges of traditional approaches, Delphix prevents missed edge cases in automated testing. It also reduces weeks of manual data relationship management work. The result is compliant, high-quality test data delivered to downstream environments — in minutes, not days. Delphix also gives self-service controls to development and testing teams so they can refresh data to the latest state, rewind after test runs, and instantly share copies.

Get Complete Datasets Without the Storage Costs

With Delphix, there is no need to be limited to a subset: deliver complete, virtual data copies into test environments. Virtual copies function like physical ones but use a fraction of the storage space. Delphix’s advanced data virtualization decreases storage costs by up to 80%.

See How Delphix Eliminates the Need for Subsetting

Find out why industry leaders are adopting the next generation of test data management solutions. See for yourself how Delphix automates delivering high-quality, compliant test data. Request a no-pressure demo today.

Go From Subsetting to Modern Test Data [Demo]

*IDC Business Value White Paper, sponsored by Delphix, by Perforce, The Business Value of Delphix, #US52560824, December 2024