Enterprise System Integration Patterns
Enterprise integration is the task of making separate applications work together to produce a unified set of functionality. Some applications may be custom developed in-house while others are bought from third-party vendors. The applications probably run on multiple computers, which may represent multiple platforms, and may be geographically dispersed. Some of the applications may be run outside of the enterprise by business partners or customers. Some applications may need to be integrated even though they were not designed for integration and cannot be changed. These issues and others like them are what make application integration difficult. This chapter will explore the options available for application integration.
Data Integration Pattern?
A data Integration pattern is the process of integrating data in a standardized Method. It involves transforming, moving, and consolidating data in all forms.
Data Integration patterns can be divided into five categories:
– Data Migration Pattern
– The Broadcast Pattern
– Bi-directional Pattern
– Correlation Pattern
– Aggregation Pattern
Types of integration patterns in enterprise systems
1. Point-to-point integration
Point-to-point integration, often called peer-to-peer integration or P2P integration, is all about connecting two separate software applications or systems directly so they can share data without needing intermediaries.
This process typically involves a one-to-one connection between two endpoints, which can be software applications, databases, or even hardware devices. Unlike more centralized methods, like hub-and-spoke or middleware approaches, P2P integration creates direct links between individual systems.
Essentially, it establishes dedicated connections tailored to the unique data exchange needs of the systems involved. For example, if you want to connect three systems, you’d need to create three separate connections—one for each pair—ensuring that each link only transfers the specific data required.
2. Hub-and-spoke integration
In a hub-and-spoke integration model, the applications act as spokes, while the integration middleware serves as a neutral hub. This middleware plays a key role by efficiently routing messages to their intended destinations, so the spoke systems don’t need to worry about the specific systems sending or receiving the messages. No matter where the messages come from or where they’re going, this approach ensures smooth communication between all the connected systems.
3. Publish-subscribe pattern
The Pub/Sub (Publisher/Subscriber) model is a messaging pattern in software architecture that makes asynchronous communication between different components or systems easier. In this setup, publishers create messages, and subscribers receive and process them.
Here are some key points about the Pub/Sub model:
- Publishers: These are the entities that generate and send out messages.
- Subscribers: These are the ones who receive and act on those messages.
- Topics: Think of these as channels or categories where messages are published.
- Message Brokers: These intermediaries handle the routing of messages, ensuring they get from publishers to subscribers smoothly.
4. Service-oriented integration
Service-oriented architecture (SOA) is all about making software components reusable and able to work together seamlessly through service interfaces. By using common interface standards and a specific architectural pattern, services can be quickly integrated into new applications.
One of the great benefits of SOA is that it takes a lot of work off the application developer’s plate. Instead of having to reinvent the wheel by redeveloping or duplicating existing functions, developers can leverage existing services without needing to worry about how to connect everything.
Each service in an SOA contains the code and data needed to perform a complete business function—like checking a customer’s credit, calculating a monthly loan payment, or processing a mortgage application. The interfaces of these services allow for loose coupling, meaning they can be called upon without requiring deep knowledge of how they’re implemented. This flexibility helps streamline development and improve efficiency.
5. Data integration patterns
A data integration pattern is a standardized way to bring together different types of data. Data integration involves moving, transforming, and consolidating data in various forms. These patterns help streamline the process, ensuring that data is synchronized and easily accessible.
Here are the five main data integration patterns:
1. Migration
Migration involves transferring data from one source to a destination system at a specific time. The data is filtered based on the migration’s goals and transformed into a standard format before being copied over. Once the data reaches the destination, it’s checked to ensure everything is accurate. This process is flexible, working with different tools and systems while maintaining the integrity of the data, even amidst changes in the organization’s IT landscape. Migration is also designed to handle large volumes of data efficiently.
2. Broadcast
Broadcast refers to the continuous flow of data from a single source to multiple destination systems. It operates in a transactional manner, processing only the data that has changed since the last update. This pattern ensures that events in the source system are automatically sent to various destination systems in real-time, without needing human intervention. Depending on the importance of the event, broadcasts can be triggered by the event itself or scheduled to occur regularly.
3. Bi-directional Synchronization
Bi-directional synchronization merges two datasets from different systems, allowing them to function as a single entity while still maintaining their individual identities. This pattern ensures both systems have a consistent, real-time view of the data, which is especially useful for organizations using different applications simultaneously.
4. Correlation
Correlation is a specific type of bi-directional sync that focuses on the relevant data at the intersection of two datasets. By only syncing data that matters to both systems, this pattern simplifies and streamlines the integration process.
5. Aggregation
Aggregation combines data from multiple systems into one central system, providing a unified view of real-time data. It retrieves, merges, and transforms data from various sources without needing an additional combined system.
For integration solutions, SnapLogic is a top choice. Its data pipeline patterns—pre-built, reusable integration pipelines that you can configure easily—are one of SnapLogic’s best-kept secrets.
6. API integration patterns
APIs can be deployed from a variety of places—whether that’s your on-premises systems, SaaS applications, or third-party services. To get started, it’s crucial to have a clear understanding of which APIs you plan to use. This will guide you in exploring their design and usage patterns more deeply. The architecture for your API management solution will depend on where most of your exchanges occur. If you primarily work on-premises, your API management should be set up there. If you’re mostly in the cloud, then it should be cloud-based. And if you use both? You might opt for a hybrid setup that combines cloud and on-premises elements.
When starting an API management project, it’s easy to overlook opportunities with third-party APIs, often leading to a cloud-focused deployment. However, there’s a wealth of existing APIs that can provide unique functionalities for your business. These third-party APIs can be accessed directly from a browser, your corporate website, or through your API management solution, including for API composition.
It’s especially important to consider the pricing model and the service-level agreement (SLA) for these APIs. For example, if you need an SLA of 99.9%, relying on an API with an SLA of 99.5% won’t meet your needs.
Once you’ve thought through your integration strategy, you can move on to designing your API integrations, keeping in mind the key foundational patterns you should follow.