It is a well-known fact that most organizations currently utilizes a substantial amount of legacy systems that require prompt upgrading. Throughout my career, I have been involved in the process of modernizing and migrating these systems to new technologies. As we navigate the dynamic and ever-evolving business landscape of the digital age, it is imperative that we continuously strive to upgrade and streamline our systems to meet the demands of the market.
One of the most important problems we are currently dealing with is the maintenance and modernization of our legacy systems. Although these systems have helped us in the past, they are now getting in the way of our ability to grow and stay competitive. We must now migrate these systems to more modern and efficient architectures, like microservices, to make sure we stay on top of trends and meet customer demands.
The connection between service and data, which should show us a clearly defined interface or contract, supports the idea of a bounded context and a sharing-less architecture in which each service and the data it uses are shared and utterly independent of all other services. In this article I will try to explain how we can avoid one of the most common anti-patterns during the process of migration, where we are focused on data first.
Due to this limited context, it is easy and quick to design, test, and deploy applications because they only depend on a few other things. In fact, one of the benefits of microservices is that they let you build many small, distributed, single-purpose services that each take care of their tasks.
When transitioning from a monolithic to microservices architecture, it’s a frequent mistake to prioritize data migration. This approach, referred to as the “data-driven anti-pattern”, poses a significant risk to the business by incurring higher economic costs and increasing the chances of a failed migration. Moving data and service simultaneously is not an effective strategy. This can lead to the wrong path, and it can greatly increase the risk.
- Any effort to turn microservices into something else must have two main goals. First, a monolithic application’s functions should be broken up into separate, specialized services called “microservices.”
- As a second goal, the monolithic data must be moved to smaller databases or, if it’s just one database, to different schemas held by each service.
Image below shows what a typical migration looks like when both the service code and the data that goes with it are moved at the same time.
You’ll see that the monolithic architecture is made up of three different services and databases. Since each service and the data that goes with it need to have a key-bounded context set up, this migration process makes sense. This common practice is starting to cause problems, though, and is pushing you toward the data-driven anti-pattern of migration.
Navigating the Challenges of Service Migration
After giving this some thought, we’ll say that the key problem with this kind of migration is that it’s rare for each service to work perfectly the first time.
Engineers can often change the level of detail in their services, but it’s always best to start with a more detailed service once you understand it better and then subdivide it further if you need to. Take a look at the image above, which shows the migration. The services are on the left.
Imagine that after learning more about your services, you realize that you need to split into two more manageable services because the current implementation is too rigid.
Alternatively, you may find that the two services on the left need to be combined because they are too detailed. In both scenarios, you need to undertake two migration tasks — one for the database and one for the service functionality. Image below provides an illustration of this scenario.
We have to keep on mind data is not an asset of an application, it is an asset of the company.
One can learn more about the risks and problems that come with constantly moving data. Data migrations are much harder and more likely to go wrong than source code migrations. In an ideal world, each service should only have to move data once. The first step to avoiding this anti-pattern is to be aware of the risks of data transmission and give more importance to data than to functionality.
Headless systems as first class citizen
By offering a decoupled architecture that separates the front-end and back-end of a system, headless systems like Penzle, Kotent by Kentico (Kentico.ai), or Contentful can assist with migration of data. As a result, there is more flexibility when transferring data to a new system because the front-end can run smoothly even as the back-end is being updated.
In addition, for example, Penzle content management API enables simple data access and management, facilitating the transfer of data between systems. The API also allows for data to be retrieved in a structured format, which can facilitate the migration process where you can have different versions and keep your data safe with ability to make undo anytime.
We now have the chance to utilize some of the benefits of the headless system because we are working to modernize the new software. Headless systems, such as Contentful, Penzle or Kenitco.ai, can be easily scaled up as needed because they are designed to handle large amounts of data. On the other hand, since headless systems are built to be quick and effective, speed and performance are very high, which can be useful for getting content to end users quickly.
Legacy systems often only provide one type of data for each customer. However, by using some of headless platfrom features, we can improve the user experience with personalized content. This is one of the key feature of headless platforms and can boost user engagement and retention. Companies like Penzle and Kenitco.ai use machine learning and AI to personalize content and enhance the user experience.
Additionally, we want to test against multi-channel apps because users today have multiple devices. Nearly all headless systems also let you deliver the same content to various channels, like websites, mobile apps, and other platforms, which can be useful for reaching a larger audience.
A headless system like Penzle, Kentico.ai or Contenful by can be a valuable tool in avoiding the anti-pattern of over-bounded contexts during software migration. The first step should be to focus on migrating the functionality of the service, while keeping in mind that adjustments to the level of granularity may be needed as the migration process progresses.
Once the service functionality has been migrated, the data migration process can begin, with the goal of creating a limited context between the service and the data. Using a headless system like Penzle or Kenitco.ai allows for more flexibility and scalability during the migration process, and the decoupled architecture can make it easier to integrate the system with other systems, and to retrieve data in a structured format.
In conclusion, a key aspect of successful software migration is achieving the correct level of granularity for the service and its related data. By focusing on migrating the service functionality first, adjustments can be made as needed. Once the appropriate level of granularity has been determined, the data migration process can proceed with the goal of creating a bounded context between the service and the data. I believe that this approach can help ensure a smooth transition with minimal disruptions.