Data integration is the process of combining data from different sources into a unified view, allowing organizations to analyze, manage, and access data efficiently. It involves collecting, transforming, and consolidating data from various systems, databases, and applications into a single, cohesive dataset. This unified dataset can then be used for reporting, analytics, business intelligence, or operational purposes.
Key components of data integration include:
- Data Extraction: Retrieving data from various sources, such as databases, APIs, cloud storage, or file systems.
- Data Transformation: Converting data into a standard format, ensuring consistency, and applying business rules to make the data suitable for integration.
- Data Loading: Storing the transformed data into a target system, which can be a data warehouse, data lake, or other storage solutions.
- Data Orchestration: Managing workflows to ensure data is moved and transformed in an organized manner.
- Data Quality Management: Ensuring the accuracy, completeness, and reliability of data throughout the integration process.
Common approaches of data integration include: