What is Data Integration?
Data Integration is the process of combining data from different sources and formats into a unified and consistent view, making it easier to analyze, visualize, and derive insights from the data. Data integration can involve various tasks, such as data cleaning, data transformation, data deduplication, and schema matching. The main goal of data integration is to provide a comprehensive and consistent representation of the data that can be used for decision-making, reporting, and analysis.
Data Integration Techniques
There are several techniques for data integration, including:
- Extract, Transform, Load (ETL): A traditional data integration process that involves extracting data from various sources, transforming the data into a unified format, and loading the transformed data into a central data warehouse or database.
- Data virtualization: A technique that provides a unified view of the data without physically moving or copying the data from the original sources.
- Data federation: A technique that integrates data from multiple sources by creating a virtual database that can be queried and analyzed as if it were a single data source.
- Data lake: A centralized repository that stores raw data from various sources in its native format, allowing for flexible and scalable data processing and integration.
Resources for Learning More about Data Integration
To learn more about data integration, check out the following resources: