Data Integration Guide: Techniques, Technologies, and Tools

Data integration is a process that combines data from disparate source systems into a single unified view, which can be accomplished through manual integration, data virtualization, application integration, or moving data to a unified destination. The benefits of data integration include providing a single source of truth, leveraging technology designed for analyzing big data, transforming data in a single location, improving security, and reducing operational risks. ETL (extract, transform, load) and ELT (extract, load, transform) are the two most popular data integration techniques. Data integration can be used to combine data from multiple source systems into databases, data warehouses, data lakes, or data lakehouses, each with its own strengths and weaknesses depending on the use case. Selecting the best data integration tool involves considering factors such as ETL versus ELT, open-source versus closed-source, cloud-hosted versus on-premises, and number of source connectors.

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