According to Wikipedia:
Data integration involves combining data residing in different sources and providing users with a unified view of them. This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bio-informatics repositories, for example) domains.
What does it actually need to deliver Data Integration at the conceptual level? Assume where data eventually resides can be, there are a few fundamental components are needed:
- We will need a mechanism of to enable the transit from source to destination
- We will need to transform or prepare the dataset for validation purpose, performance, cost and business logic implementation purpose.
- We may need purposes.
- We will need to design a de-coupled, immutable, idempotent data processing processes to ensure it is operationally supportable at scale.
Data Integration is a core part of the data value chain for the businesses. It also enables.