Data integration is the process of combining data from different sources, formats, or structures into a unified format or structure that can be easily analyzed or queried. The goal of data integration is to provide a unified view of the data that is accurate, consistent, and up-to-date.

Data integration can involve various techniques, such as data warehousing, extract, transform, and load (ETL), and application integration.

Data warehousing involves storing data from multiple sources in a centralized repository, while ETL involves extracting data from different sources, transforming it into a common format, and loading it into a target system.

Application integration involves integrating data from different applications and systems.

There are several challenges associated with data integration, such as data quality issues, data inconsistencies, and the need for data mapping and transformation. To overcome these challenges, data integration tools and platforms have been developed that can automate many of the processes involved in data integration, such as data mapping and transformation.

Data integration is a critical component of data management, as it enables organizations to make better use of their data assets, improve data quality, and make better-informed decisions.