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Better Database Development: The Role…

Better Database Development: The Role of Test Data Management

Any organization that aims for reliable, online deployment of database changes needs a Test Data Management (TDM) strategy that allows developers to test the database early, thoroughly, and repeatedly, with the right test data. The benefits include more resilient database deployments, fewer bugs, and shorter lead time for changes.

Guest post

This is a guest post from Tonie Huizer. With 20+ years of experience Tonie likes to read, experiment, talk and write about software. In his stories on medium he explains Azure, SQL and other Microsoft technologies.

This article is part of a series on managing test data in a Flyway development:

Also relevant are the series of related articles on Database testing with Flyway.

Despite the drive toward more frequent database deployments, many teams still do not have any particular test data management (TDM) strategy in place. As developers, we are left to do the best we can, often developing and testing with a single set of test data that is far from representative of the real thing.

The result, unfortunately, is lower quality software, more bugs reaching the production system, and frequent database deployment failures. This includes failure to deploy the new database version, as well as deployments that ‘succeed’ but subsequently cause problems that affect the availability, performance, or security of the associated business applications. Often, even production hotfixes, designed to fix a previous deployment problem, cause further unanticipated issues. Again, this is usually because the hotfix was rushed out without proper testing.

This article will start by giving an overview of a test data management strategy that will allow you to address these issues. It will walk you through the steps to achieve this and explain the measurable benefits.

Overview of the TDM strategy

The following diagram sets out the basic characteristics of our test data management strategy. The workflow looks like this:

  1. Choose dataset – choose the required data set, appropriate for the sort of tests that need to be run.
  2. Provision – use the chosen test data to provision ‘dedicated’ databases, one per developer (or per branch of development).
  3. Develop or Test – each developer develops and tests code independently, before merging their work into the main development branch.
  4. Reset environment – developers should be able to reset databases, on demand, so that tests can be run consistently and repeatedly.

Test data management strategy for database development

Step-by-step to TDM

Of course, we do not need to achieve this full TDM strategy in a single leap. We can get to the benefits in smaller steps…

A. Moving to dedicated development databases

In my experience, the developers’ ability to work creatively and test their work properly is severely restricted by the requirement to use a single, shared development database. We’ve all encountered cases where that shared database environment was set up by a DBA some time ago, no-one is quite sure when, from a production backup. It hasn’t been refreshed since and while the team have a migration workflow in place to manage changes to the structure, there is no strategy for maintaining the data.

As a result, the data gradually becomes stale, lower quality, and unrepresentative of the current state of the production system. If a developer runs tests that mess up the data, the whole team is disrupted until the DBA can provide a ‘refresh’. This fear of disrupting the work of others means that testing is often sporadic or delayed to a later stage in the project and so bugs are caught later when they are harder to fix and cause delays.

All this is why I suggest that the first improvement that needs to be made is to allow each developer to use a personal development database, instance, server or container. Sometimes this is tricky to achieve, because the ‘single shared database’ requirement is usually the result of a valid logistical or security concern that we first need to overcome.

Alleviating security issues

If the production data contains personal or sensitive information, it can usually be made available only to restricted personnel, in an environment with the same security as production. If your TDM strategy includes the requirement to work with copies of production data, then it will need to provide an effective and automated way to first remove any sensitive or personal data, or ‘mask’ and obfuscate it to the point where it could never identify any individual.

The alternative would be for your TDM process to incorporate a way to generate “fake but realistic” data for development work.

Dealing with very large databases

Often, maintaining more than one or two development copies of a very large production database takes too much time and management and requires too much disk storage space.

If so, then your TDM strategy will need to provide a way to reduce the size of the distributed production data sets, such as by using data subsets, or by allowing a lightweight way to create and rapidly reset copies of the full production data set, such as by using database clones and containers. Another alternative would be the use of smaller generated data sets for development and testing.

TDM for complex data systems

Similarly, for complex Enterprise applications supported by many interdependent databases; supplying individual, dedicated copies of this system is often impractical.

In these situations, it can be a challenge to create and maintain generated test data for interlinked databases, or even just to provide and seed data that needs to be in sync between the databases. Use of technology such as ‘containerized clones’ as part of a TDM strategy helps a lot here. It allows teams to capture an image of an entire instance, consisting of any number or interconnected databases. We can then distribute a lightweight clone of that image to each developer, as a data container.

B. Resetting development and test environments

The dedicated databases must be not only easy to create but also, just as importantly, easy to remove or reset. A test run generally requires a set up that creates a copy of the database at the right version, including the data, runs the test, assesses the result, and then tears down the test set up, resetting the database to its original state, ready for the next test. Since tests don’t always pass the first time, this can mean creating and resetting a database many times, over and again.

If your TDM strategy is implemented using clones and containers then these have the advantage of an almost ‘push button’ reset process. Simply run the test, then an automated, scripted method should reset the data container back to its initial state.

An alternative is to use a script method alongside a database migration tool like Flyway. The scripted process will create an empty database at the right version, load the required dataset, run the test, teardown, repeat. A Flyway baseline migration script will create the database at the required version and then you can use Flyway callback to load the required dataset automatically. See, for example, Running Unit and Integration Tests during Flyway Migrations.

Of course, these scripts don’t have to be run by the user; pipelines for automated provisioning and testing are the solution here!

C. Creating and maintaining the right test data sets

With your own database environment in place, it’s time to get the data right, or rather to get the right data! What data and how much is really needed? Initially, your TDM strategy may only support a single method of providing dev and test databases, complete with data. You can then start to build out the strategy, adding support for all the different data sets that developers need for the different types of tests. The data sets should be easy to create and maintain, and switching between data sets should be simple.

The data you need depends on the task. If the tests require data that is highly representative of the real production data then using a copy of the production data might be the best choice. However, this assumes the data is first masked and obfuscated to comply with GDPR and any other data protection regulations for your industry. Often only a subset of the full production dataset is required, in which case you need a subsetting tool that will extract a fraction of the full production data, leaving all the structure and relations of the database in-tact.

For many types of tests that a developer will wish to run, such as unit or integration tests, use of production data is unnecessary, and also won’t necessarily contain all the different types of data required to run the tests (such as edge case data or invalid data). These tests are sometimes best served with a process that separates the management of schema and data. It will, for example, use a build script to create an empty database at the right version, complete with any required seed data. There might be cases where an empty database is used for tests, for example to test the first run of your software from the perspective of a new customer. More often, of course, you’ll then need the script to load the required test dataset.

The datasets might be created in a number of ways:

  • Curated by the developers – such as when producing data sets to test that valid but ‘edge case’ data is handled correctly or that invalid data is excluded.
  • Created using a data generation tool – to generate larger volumes of “realistic-looking” but fake data that conforms as far as possible to the distribution and nature of the live data.
  • Supplied by the business – for example integration tests often start from a standard dataset, run the process then check the result against what the business supplied as the “correct result”
  • Sniffed from production – with a tool like Extended Events, valid test data can be generated from use cases that are executed in production.

One challenge will be in maintaining all these data sets in response to changes to the database structure during development, and understanding which versions of the data are valid for which versions of the database. Whoever creates or alters one or more of the tables in a way that affects existing data must also do the work of altering the datasets.

Whichever way the test data sets are supplied, the provisioning of the right dataset (empty, generated, subset or everything) should be a highly automated self-service process. All the developer should need to do is specify what test data is needed. This can be a mandatory step during the refinement process of new work in a sprint, or an ad-hoc choice made during the development or test process.

Once an automated system for serving test data is in place, you’ll start to see measurable benefits.

Measurable benefits of a TDM strategy

A lot of the benefits of a TDM strategy come from the ability to move toward a test-driven development system, where developers run extensive database tests in their own dedicated databases (such as when working on a feature branch), before even committing the code to the main branch of development. We catch far more bugs long before they ever reach production.

Other benefits include:

  • Wider test coverage –ability to catch edge case problems, run full process checks as well as unit tests, and so on
  • Improved testing accuracy with realistic test data – I still remember the example of a software vendor delivering an upgrade of having only tested against IDs below 10. The production system had more than ten thousand IDs. The result? Down time in production, that could have been prevented.
  • Reproduce difficult production bugs, develop better hotfixes – with a TDM strategy that offers lightweight support for producing production data sets, safely.
  • Protecting data integrity/security – the ability to produce data sets that can be used to test that invalid data is prevented from entering the database, or that the database is safe from injection attacks.

With proper test data management in place the quality of your work (product) will increase. The software is now tested in a more structured way, repeatable and suitable for the scenario. As a result, you should start to measure positive changes in many of the so-called DORA metrics, and other related productivity measures.

For example, you should expect to see a measurable decrease in the number of bugs detected in Staging or Production, and in the overall change failure rate. There will also be a reduction in the time spent testing, and in the lead time for changes. This will increase your deployment frequency and time to market for new software.


A well-planned TDM strategy will support a test-driven approach to database development, where developers extensively test code in their dedicated databases before integration. It will make it easy for the team to load, switch between and maintain all the different types of test data they need for their testing. This leads to broader test coverage, improved testing accuracy with realistic data, better bug detection, and therefore to more regular and resilient deployments of higher quality database changes.


What to Read Next

If you’re interested in how to implement a TDM strategy as part of a database migration system, try Test Data Management for Database Migrations, along with related articles in the Test Data Management section of this website. Some of the advantages of using ‘disposable clones’ as a part of the TDM implementation are explained in Self-service, disposable databases for development and testing.

The following articles provide useful background to the topics covered in this article:


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