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As application systems have become more complex and distributed, the movement and communication of that data between applications has become more important. When data is moving across systems, it isn’t always in a standard format and the role of data integration makes data agnostic.
Principle of Application Orchestration
To have a better understanding of the data integration, we need to be aware of the principle of Application or Service Orchestration. This represents the scenario of integrating two or more applications and/or services together to automate a process, or synchronize data in real-time.
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An approach to integration that decouples applications from each other
Provide capabilities for message routing, security, transformation and reliability
Defines a method to manage and monitor your integrations centrally.
The Critical Nature of Data Integration
As the IT world has evolved over time, the manner and methods in which companies run and manage their critical enterprise data has changed. In earlier days of IT, all data integration efforts were performed through individualized tools that were designed and optimized to handle specific types of data (JMS, SOAP, etc).
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More and more resources are needed to allow data integration to solve the problem of moving, transforming, and consolidating information from various parts of the enterprise (systems, databases, applications, files, and web services). Within these processes, our data must also undergo cleansing, standardizing, de-duplication, manipulation, and synchronization between sources.
Data Integration Problems
Not having a common data integration tool across the enterprise will make it difficult for businesses to maintain and manage information and impede their communication patterns. For example, one department has a different way of managing customer data than another, trying to consolidate data from both departments can be difficult.
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This can lead to risky investments in time and resources to manage and maintain an infrastructure consisting of a great deal of hardware and software. Additionally, the use of certain data types can call for specific tools, thereby forcing application systems to implement integration from multiple disparate vendors (with all the known headaches associated with this implementation). When a multitude of integration solutions are deployed across the enterprise, it will result in inefficiency due to a lack of consistency and simplicity.
Data Integration Design Patterns
To make our critical enterprise data more usable and to increase its availability faster, developers can use data integration patterns to standardize the integration process. There are five data integration patterns based on business use cases.