Data integration is the process of combining and harmonizing data from multiple sources into a unified, coherent format that can be put to use for various analytical, operational, and decision-making purposes. In today’s digital landscape, organizations gather data from diverse sources, including databases, apps, spreadsheets, cloud services, and APIs. However, this data often resides in different formats and locations, leading to data silos and inconsistencies.
Here’s how a typical data integration process works:
- Data Source Identification: Identify various data sources, such as databases, spreadsheets, cloud services, APIs, and legacy systems.
- Data Extraction: Extract data from identified sources using tools or processes (e.g., querying databases, retrieving data through APIs).
- Data Mapping: Create a mapping schema to align data elements from different systems.
- Data Validation and Quality Assurance: Validate data for errors, inconsistencies, and integrity issues. Implement quality assurance processes to maintain accuracy and reliability.
- Data Loading: Load transformed data into a data warehouse or other desired destination for analysis or reporting
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