What is Data Integration? Methods, Patterns & Best Practices

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A professional-level summary covering key definitions, frameworks, and exam-relevant points.

Data Integration Methods Comparison

Method	Data Movement	Latency	Best Use Case
ETL	Extract → Transform → Load	Batch (hours)	Data warehouse loading; historical analytics
ELT	Extract → Load → Transform	Batch (minutes-hours)	Cloud data warehouses; big data
API integration	Real-time push/pull	Real-time (seconds)	Operational integration; microservices
CDC	Changed records only	Near real-time (seconds-minutes)	Database replication; streaming analytics
Data virtualisation	No movement (virtual layer)	Real-time (query-time)	Federated analytics; data access layer
Message queuing	Event-driven	Near real-time	Event-driven architecture; microservices

CDMP Exam Relevance

Data integration is a dedicated knowledge area in the CDMP exam (Data Integration & Interoperability, 6%). Key exam topics include: the integration methods and their characteristics, the difference between ETL and ELT, the role of data integration in supporting analytics and master data management, integration patterns (hub-and-spoke, point-to-point, ESB), and the governance considerations for data integration (data lineage, data quality, metadata management). Understanding integration is also important for Data Warehousing & BI questions.

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