The pleasures of having DTAP


No, not Diphtheria, Tetanus, and Pertussis (vaccine), but Development, Test, Acceptance, and Production (DTAP): different environments that, together with a well-working release management process, provide a way to get higher quality and reduced risks in production. DTAP is an important cornerstone for a larger infrastructure architecture as it provides environments that are tailored to the needs of many stakeholders.

What are these four environments?

Let's go over the four environments one by one with a small introduction to their purpose. I'll cover more specific use cases further down in this post.

The Development environment is a functionally complete environment on which the development of products or code is done. It should have the same technologies in place and very similar setups (deployments) so that developers are not facing a too different environment. Too much difference might imply different behavior, which is contra-productive. The environment is accessible by developers and testers, and is mainly development-oriented.

Products or code that are being developed will be visible and used in this environment. Developers and engineers hardly have any threshold to reach for deploying or modifying code here.

The Testing environment is used when the development has reached a phase where the product or code has passed a minimum of quality. Unit tests succeed, the code builds fine, and the developer has indicated that the code or product is ready for wider testing (hence the name).

A testing environment generally applies a multitude of tests, most of them (hopefully) automated, but with a strong dependency on testers (also known as Quality Assurance engineers or QA engineers) to find issues.

The automated tests focus on integrations, regression testing, security testing, etc., and provide insights into the new builds or products which the development team can take up and iterate over to improve the product.

The Acceptance environment is a production-like environment, not just for one product, but for the entire business or business unit: the same setup, the same infrastructure, the same foundations, the same application portfolio, the same integrations, etc. This environment intends to validate that the product is fully ready to be released. It is often also abbreviated as the User Acceptance Testing (UAT) environment.

While the testing environment is generally approached by QA engineers, the acceptance environment should be more tailored to business testers. When developers or engineers introduce a feature, the stakeholders that requested that feature use the acceptance environment to accept if the coming release fulfills their request or not.

As the environment is production-like in its entirety (and not just for a single product), it is a prime target for executing performance tests as well. Cross-product dependencies and processes (like migrations) are validated here too.

The Production environment is the environment in which the product or code "goes live", where the customers use it. That does not mean that a product put in production is immediately accessible for the customers: there is still a difference between deployment (bring to production), activation (enable usage), and release (use by customers).

While DTAP is well-known in larger organizations, there are some challenges or misconceptions that I would like to point out, and which I discuss further down:

  • Environments are more than just the systems where the code is deployed to. Each environment has production services associated with it.
  • A prime challenge to implementing DTAP is the cost associated with it. But it does not need to be as expensive as you think, and DTAP implementations often have a positive business case.
  • Agile methodologists might find DTAP to be old-style. They are correct that many implementations are prohibitive towards fast deployment and release strategies, but that isn't because DTAP is conceptually wrong.
  • Not all environments need the same data. On the contrary, a proper DTAP design likely uses separate datasets in each environment to deal with the security and regulatory requirements.

Conceptual environments still require production services

The purpose of each environment is strongly tied to the 'phase' in which the code or the product resides in the development lifecycle. That means that these environments have a strong focus on that product or code, and not on the services that are needed.

Indeed, a development environment also entails services that are already 'in production', like a usable workstation, development services like code repositories and build systems, ticketing services, and more. A testing environment requires test automation engines, regression test frameworks, security tools, and more. All these services are production-ready - and they often have their own DTAP environments as well.

It is a common misconception that a development-oriented system or service has a lower SLA or lower risk profile than production, and infrastructure architecture should make clear that there is a difference between the systems that host the products or code under review and the systems that facilitate the functionality needed within the environment.

Static cost is a major inhibitor for implementing DTAP

Smaller companies or organizations might be hesitant to introduce DTAP environments as it might be cost-prohibitive. While it is true that, from a 'static' view, a DTAP environment costs more than a production-only environment, you need to consider the impact of implementing DTAP in the processes.

The main purpose of DTAP is not to make your CFO angry, but to improve the quality of your production environment (and usage). Ask yourself: how costly is it when your production systems go down, or when your customers complain and you need to fix things... Do you update code directly on the server(s)? What if a security patch is rolled out and suddenly prevents your customer-facing application from working?

While DTAP mentions four environments, some companies settle with three, and others with five or more. Perhaps your testing and acceptance are done by the same people, and you do not have many automated testing facilities at hand. Splitting your pre-production environments into multiple environments doesn't make sense yet, and you might first want to focus on improving your testing maturity in general.

If you want to make the case for DTAP, consider the use cases or scenarios that have visibly disrupted your business and how/where the environments would have helped. In many cases, you'll notice that there is a positive business case for a step-wise move towards DTAP.

Furthermore, a proper design of these environments will facilitate an economical view towards DTAP.

  • You can use pay-as-you-use environments (as is commonly the case in the public cloud) which you only activate when you do your testing. If you have a 24/7 customer-facing service in production, that doesn't mean that your acceptance environment has to be 24/7. Yes, it should be as much production-like as possible, but if it isn't doing anything outside business hours, then you don't need it running outside business hours.
  • Commercial products often have distinct terms and conditions for non-production usage. You can have databases in production with a premium, gold service level agreement, while having the same database in the other environments with a low, bronze service level agreement towards that vendor: cheaper, but technically the same.
  • Abstraction and virtualization technologies allow for better control of the resources that are being used. For instance, you can have an acceptance environment that is only at 20% of the resources of production for day-to-day validation, and then increase its resources to 100% for load testing periods. If these environments are not in a pay-as-you-use model, shifting resources from one environment to another allows for controlling the costs.
  • Security controls in these environments might be different as well, assuming that these environments have different data needs: if you use fictitious data in development and testing, and anonymized data in acceptance, then the investments on, say, data leakage controls might be different for these environments.

Hence, while DTAP is at first glance a costly approach, the actual case is that it is positive for the company.

DTAP is not inefficient, but some implementations are

In a world where "Release fast, release often" is the norm, having a rigid DTAP strategy might be contra-productive. However, this is more an implementation concern than a conceptual one. If I consider the downsides that Christiaan Verwijs mentions in his post, I feel that I can safely state that this is not due to the lifecycle of the code or product, but rather the choices that a company has made while assessing and implementing a release strategy.

There is no need to bundle and make bulk releases with DTAP. You can perfectly design an environment where DevOps teams can release easily to production. More so, the bulk release strategy is frequently the result of an application design constraint, not a deployment constraint.

Development methodologies and DTAP environments do need to be tailored to each other. The purpose of DTAP is to facilitate the quality of products and code, and thus should be tailored towards efficient and qualitative development processes. In many environments, DTAP is synonymous with "infrastructure operations" and that's a wrong approach. Operations-oriented teams (like Site Reliability Engineers (SREs)), development-oriented teams, and DevOps teams all have the same benefits from DTAP.

Some might state that an acceptance environment is no longer suitable in the modern age, as they can deploy products and code to production without losing the benefits of the acceptance environment. With blue/green deployments or canary releases, you can enable business testers and stakeholders to validate new features or code before releasing it to the wider public.

To accomplish this properly, however, the platform that is used will balance resources accordingly, and you're conceptually implementing an (albeit temporary) acceptance environment in an automated way. This is an implementation choice and has to be balanced against the requirements that the organization has.

For instance, if you work with sensitive data, you might not be allowed to use this data during testing. In Europe, the General Data Protection Regulation (GDPR) is a strong regulatory requirement for dealing with sensitive data. It isn't a playbook though: companies need to evaluate how and where data is used, and perhaps the balance made by the company allows, if not with explicit consent, to use data unmodified for acceptance testing. But if that isn't the case and your acceptance tests need to use sanitized data, then having separate environments is likely more sensible (although different implementations exist that allow for anonymization in production as well - they're, however, not as easy to implement).

Plus, DTAP does not imply that production is doing everything in a single unit of work: Deploy, Activate and Release. You can still perfectly position those tasks in production while having an explicit acceptance environment.

Separate datasets in each environment make sense

For regulated companies and organizations, security officers might want to use the DTAP distinction to focus on data minimization strategies as well. As mentioned before, the GDPR is a strong regulatory requirement whose alignment can be facilitated by a well-designed DTAP approach.

You can use fictitious data in development and testing, with development using datasets that developers use for validating the specific functionality they are working on (and preferably share and put alongside the code and products), whereas testing uses a coherent but still fictitious dataset. I use "coherent" here as an indication that the data should be functionally correct and integer: a (fictitious) person record in the customer database in the testing environment should be mapped to the (fictitious) calls or other interactions that are stored in the support database (also in the testing environment) and the (fictitious) portfolio that this (fictitious) person has in the product database (in the testing environment).

Don't underestimate how powerful, but also how challenging a good fictitious dataset is.

For acceptance testing, perhaps the company decided that anonymized data is to be used. Or it uses pseudonymized data (which is a weaker form) with additional technical controls to prevent leakage and attacks (including inference) that try to deduce the origin of the data.

Again, these are choices by the company or organization, which need to be taken with the risk and business stakeholders.


DTAP is a sensible approach for improving quality in products and code. While it isn't the holy grail for quality assurance, it has solid foundations that many larger companies and organizations feel comfortable with. The implementation details make or break how well-adjusted the DTAP approach is for modern development processes and regulatory requirements.

Feedback? Comments? Don't hesitate to drop me an email, or join the discussion on Twitter.