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A data integration service is programming that can connect to a source system, extract data, transform it and incorporate it in a target system along with data from other source systems. The target system can then be used as a golden record (single source of truth) for other applications and computing systems.
Data integration service strategies include:
- Extract, Transform and Load — extracted data is transformed by a middleware ETL server before it’s transferred to the target system.
- Data Replication — changes to the source system are replicated on the target system in real-time.
- Publish-Subscribe – downstream systems subscribe to a data integration service that will periodically update the target system.
- API & Web Services – used to build a loosely coupled architecture that can accommodate multiple request/response-based data services simultaneously.
Techopedia Explains Data Integration Service
Building a single data pipeline can be difficult today because it requires someone to manage business rules, rules for transforming data and rules for managing configuration drift, job scheduling and data transfers in multiple settings. Cloud-based data integration services can expedite data integration significantly by automating workflows that directly impact data availability.
How Data Integration Services Work
In the past, data integration services were so complicated that building them often required help from data engineers who knew how to code. Today, cloud-based data integration services are designed to be managed through a low-code/no-code (LCNC) dashboard and use application programming interfaces (APIs) to extract and transfer data.
The graphical user interface (GUI) dashboard, which hides back-end complexities, allows users to drag and drop icons that represent different types of dataflows, pipelines or workflow designs. Once a service has been assembled, it can be tested in a sandbox environment before executing it in the production environment.
To keep data integration services as simple as possible, experts recommend avoiding:
- Designs that involve creating and managing too many moving parts.
- Code-heavy approaches that fail to take advantage of automation.
- Legacy programs that were never designed to support native or hybrid cloud architectures.
- Integration service engines that are designed to accommodate a specific, narrow use case (e.g., application integration).
