Data integration is the process of combining data from disparate source systems into a single unified view, moving data to a Single Source of Truth.
Manual Integration
Manual integration involves analysts manually logging into source systems, analyzing and/or exporting data, and creating reports.
Disadvantages of Manual Integration:
- Time-consuming: The process requires significant time investment.
- Security Risks: Analysts need access to multiple operational systems.
- Performance Issues: Running analytics on non-optimized systems can interfere with their functioning.
- Outdated Reports: Data changes frequently, leading to quickly outdated reports.
Data Virtualization
Data virtualization is a method that allows access to data without needing to replicate it, providing a unified view of data from multiple sources.
Application Integration
Application integration links multiple applications to move data directly between them.
Methods of Application Integration:
- Point-to-Point Communications: Direct connections between applications.
- Middleware Layer: Using tools like an Enterprise Service Bus (ESB).
- Application Integration Tools: Specialized tools for integrating applications.
Disadvantages of Application Integration:
- Data Redundancy: May result in multiple copies of the same data across systems.
- Increased Costs: Managing multiple copies can lead to higher costs.
- Point-to-Point Traffic: Can create excessive traffic between systems.
- Performance Impact: Executing analytics on operational systems may interfere with their functioning.