3.2 Using Connectors and API Integration
Extracting data from cloud sources is a key part of the modern ETL process. It involves using specialized tools to retrieve raw data from various cloud-based systems and move it to a temporary staging area for further processing
🡆Connectors: These are pre-built software components designed to interact with a specific data source or destination. They abstract away the technical details of connecting to a system, such as authentication and data format, providing a simplified interface for users.
- Example: An ETL tool provides a connector for Google Analytics. A user simply inputs their credentials, and the connector handles the complex API calls to retrieve website traffic data, making it easy for a non-programmer to get the data they need.
🡆API Integration: For sources that don’t have a pre-built connector, direct API integration is used. This involves making programmatic requests to a system’s API to retrieve or send data . APIs often allow for real-time or near-real-time data retrieval, which can be a key advantage over traditional batch-oriented connectors .
- Example: A company has a custom-built internal application that exposes a REST API for its data. The data team uses a custom API integration to programmatically pull data from this application, as a pre-built connector does not exist for it.
Relationship and Hybrid Approaches:
🡆Connectors often leverage APIs: Most pre-built connectors are actually built on top of the underlying APIs of the systems they connect to. They simply make the complex API calls behind the scenes for the user .
🡆Hybrid Solutions: Modern ETL solutions offer a hybrid approach, providing a library of pre-built connectors for common sources (like Salesforce, Google Analytics) while also allowing users to build custom API integrations for unique or proprietary data sources .