Understanding Integration Design Patterns: Integration design patterns serve as reusable templates for solving common integration problems encountered in software development. They encapsulate best practices and proven solutions, empowering developers to architect complex systems with confidence. These patterns abstract away the complexities of integration, promoting modularity, flexibility, and interoperability across components.
Pub/Sub Integration decouples producers of data (publishers) from consumers (subscribers) through a central message broker. Publishers broadcast messages to the broker, which then distributes them to interested subscribers. This pattern fosters loose coupling and enables scalable, event-driven architectures.
Message Broker Integration employs a dedicated intermediary, known as a message broker, to facilitate communication between disparate systems. The broker receives, routes, and transforms messages, ensuring reliable delivery and seamless interoperability. This pattern promotes decoupling, resilience, and flexibility in distributed environments.
Migration is the act of moving data from one system to the other. A migration contains a source system where the data resides at prior to execution, a criteria which determines the scope of the data to be migrated, a transformation that the data set will go through, a destination system where the data will be inserted, and an ability to capture the results of the migration to know the final state vs the desired state.
time), basis.
Whenever there is a need to keep our data up-to-date between multiple systems across time, you will need either a broadcast, bi-directional sync, or correlation pattern. The broadcast pattern, like the migration pattern, only moves data in one direction, from the source to the destination. The broadcast pattern, unlike the migration pattern, is transactional.
The bi-directional sync data integration pattern is the act of combining two datasets in two different systems so that they behave as one, while respecting their need to exist as different datasets. This type of integration need comes from having different tools or different systems for accomplishing different functions on the same dataset.
For example, you may have a system for taking and managing orders and a different system for customer support. You may find that these two systems are best of breed and it is important to use them rather than a suite which supports both functions and has a shared database. Using bi-directional sync to share the dataset will enable you to use both systems while maintaining a consistent real-time view of the data in both systems.
The correlation data integration pattern is a design that identifies the intersection of two data sets and does a bi-directional synchronization of that scoped dataset only if that item occurs in both systems naturally. This is similar to how the bi-directional pattern synchronizes the union of the scoped dataset, correlation synchronizes the intersection.
In the case of the correlation pattern, those items that reside in both systems may have been manually created in each of those systems, like two sales representatives entering the same contact in both CRM systems. Or they may have been brought in as part of a different integration. The correlation pattern will not care where those objects came from; it will agnostically synchronize them as long as they are in both systems.
Aggregation is the act of taking or receiving data from multiple systems and inserting into one. For example, customer data integration could reside in three different systems, and a data analyst might want to generate a report which uses data from all of them. One could create a daily migration from each of those systems to a data repository and then query that against that database. But then there would be another database to keep track of and keep synchronized.
In addition, as things change in the three other systems, the data repository would have to be constantly kept up to date. Another downside is that the data would be a day old, so for real-time reports, the analyst would have to either initiate the migrations manually or wait another day. One could set up three broadcast applications, achieving a situation where the reporting database is always up to date with the most recent changes in each of the systems. But there would still be a need to maintain this database which only stores replicated data so that it can be queried every so often. In addition, there will be a number of wasted API calls to ensure that the database is always up to X minutes from reality.
Most Common Integration Design Patterns:
Point-to-Point Integration:
Point-to-Point Integration involves establishing direct connections between individual components. While simple to implement, this pattern can lead to tight coupling and scalability issues as the number of connections grows. Visualizing this pattern, imagine a network of interconnected nodes, each communicating directly with specific endpoints.Publish-Subscribe (Pub/Sub) Integration:
Pub/Sub Integration decouples producers of data (publishers) from consumers (subscribers) through a central message broker. Publishers broadcast messages to the broker, which then distributes them to interested subscribers. This pattern fosters loose coupling and enables scalable, event-driven architectures.
Message Broker Integration:
A message broker is software that enables applications, systems and services to communicate with each other and exchange information.
Message Broker Integration employs a dedicated intermediary, known as a message broker, to facilitate communication between disparate systems. The broker receives, routes, and transforms messages, ensuring reliable delivery and seamless interoperability. This pattern promotes decoupling, resilience, and flexibility in distributed environments.
Data integration pattern
- Migration (Data integration pattern 1) :
- Broadcast (Data integration pattern 2):
time), basis.
Whenever there is a need to keep our data up-to-date between multiple systems across time, you will need either a broadcast, bi-directional sync, or correlation pattern. The broadcast pattern, like the migration pattern, only moves data in one direction, from the source to the destination. The broadcast pattern, unlike the migration pattern, is transactional.
- Bi-directional sync (Data integration pattern 3) :
The bi-directional sync data integration pattern is the act of combining two datasets in two different systems so that they behave as one, while respecting their need to exist as different datasets. This type of integration need comes from having different tools or different systems for accomplishing different functions on the same dataset.
For example, you may have a system for taking and managing orders and a different system for customer support. You may find that these two systems are best of breed and it is important to use them rather than a suite which supports both functions and has a shared database. Using bi-directional sync to share the dataset will enable you to use both systems while maintaining a consistent real-time view of the data in both systems.
- Correlation Data (integration pattern 4):
The correlation data integration pattern is a design that identifies the intersection of two data sets and does a bi-directional synchronization of that scoped dataset only if that item occurs in both systems naturally. This is similar to how the bi-directional pattern synchronizes the union of the scoped dataset, correlation synchronizes the intersection.
In the case of the correlation pattern, those items that reside in both systems may have been manually created in each of those systems, like two sales representatives entering the same contact in both CRM systems. Or they may have been brought in as part of a different integration. The correlation pattern will not care where those objects came from; it will agnostically synchronize them as long as they are in both systems.
- Aggregation (Data integration pattern 5):
Aggregation is the act of taking or receiving data from multiple systems and inserting into one. For example, customer data integration could reside in three different systems, and a data analyst might want to generate a report which uses data from all of them. One could create a daily migration from each of those systems to a data repository and then query that against that database. But then there would be another database to keep track of and keep synchronized.
In addition, as things change in the three other systems, the data repository would have to be constantly kept up to date. Another downside is that the data would be a day old, so for real-time reports, the analyst would have to either initiate the migrations manually or wait another day. One could set up three broadcast applications, achieving a situation where the reporting database is always up to date with the most recent changes in each of the systems. But there would still be a need to maintain this database which only stores replicated data so that it can be queried every so often. In addition, there will be a number of wasted API calls to ensure that the database is always up to X minutes from reality.
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