Top 3 Data Integration Patterns Explained
Users can use three data integration patterns to harness the true potential of data: Migration, bi-directional sync, and correlation.
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Data is the fuel of the digital economy. Though it is deemed extremely essential, it can sometimes be harder to access, orchestrate, and interpret. When data is traversing across systems, it is not always in a standard format; data integration has the objective to make data agnostic and usable, so it can be handled properly. For making data usable and available more quickly, data integration patterns can be built to standardize the entire process of integration.
Similar to hiking trails, integration patterns are built and discovered based on use. Patterns are always present in degrees of perfection. However, it can be optimized or adopted based on needs and requirements. Business users can perceive the business use case as an instantiation of the pattern.
Our research has given rise to three data integration patterns, around which users can build templates based on business use cases.
Migration involves the process of moving a particular set of data at a point from one system to another. It entails a source system where the data is kept before execution. This is a criterion that determines the scope of the data migration, a transformation process data goes through, a final destination system where the data will be appended, and the ability to captivate migration results to identify the final state vs the desired state.
This integration pattern most commonly occurs when users are being transported from one system to another or when an instance is moved to another instance, etc.
Migrations can be useful for data systems and organizations with data operations. Users invest a lot of time maintaining data, and migration allows them to keep that data agnostic. If migration is not employed, organizations are likely to lose data and eventually lose a competitive advantage.
This data integration pattern involves unifying two datasets in two different systems. This combined datasets behave like one but exist as different. The need for bi-directional sync comes from having distinct tools for achieving various functions on the same dataset.
Say, for example, a business can have a system for receiving or managing orders and another system to facilitate customer support. In this case, these two systems are important to use them rather than a suite that bolsters both functions. With this integration pattern, companies can share datasets to incorporate both systems at the same time – with ease and speed.
Bi-directional sync is helpful for businesses to execute two different functions in real-time. For example, a salesperson must know the delivery status, but they need not be aware of where the delivery is being made. In a similar line, the delivery person must know the customer’s name, but they don’t have to know how much the customer has paid for it. Bi-directional sync allows both these individuals to gain a real-time view without difficulty.
This data integration pattern involves a design that recognizes the intersection of two different datasets. It also performs a bi-directional synchronization of a dataset. This is similar to how the directional pattern, only it synchronizes the intersection and not the union of datasets. During correlation, the two datasets that may have formed manually is each of the two systems. Such as two sales representatives.
This pattern will not consider where the objects hail from. It will synchronize these objects agnostically. It is useful when extra data proves costly for users. It allows users to remove unnecessary data. It saves a lot of effort both during integration and report generation as it enables users to synchronize information.
The correlation data integration pattern is tremendously useful in cases where users have two distinct groups that are attempting to share data only if they both entail a record representing either the same person or item in actuality. We’ll take an example to demonstrate this. Suppose a hospital group has two medical units in the same city. In such a scenario, users might like to share data between the two units. If a patient relies on either hospital, they will have to upgrade the data record of the type treatment they received in both the hospitals. To perform this integration, create two broadcast pattern integrations. This will synchronize data even though users have two integrations to take care of.
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