Common data sources for ETL (Extract, Transform, Load) extraction include relational databases, flat files, and APIs. These sources are foundational because they cover structured, semi-structured, and real-time data needs, which are critical in most data integration workflows. Here’s a breakdown of each category and how they’re used:
Relational Databases are a primary source for structured data extraction. Systems like MySQL, PostgreSQL, Oracle, and SQL Server store data in tables with defined schemas, making them predictable for querying. ETL processes often use SQL to extract data directly or via connectors (like ODBC/JDBC). For example, an e-commerce platform might extract daily sales records from a PostgreSQL database to analyze trends. Transactional databases are especially common here, as they house operational data that needs to be aggregated for reporting or analytics.
Flat Files, such as CSV, Excel, XML, or JSON files, are widely used for data exchange or legacy system integration. CSV files are simple to generate and parse, making them popular for batch processing. For instance, a payroll system might export employee hours as CSV files nightly for ETL ingestion. However, flat files lack real-time capabilities and can become cumbersome with large datasets. XML and JSON files add structure through hierarchies, useful for configurations or semi-structured data. These files often require validation (e.g., checking for missing fields) before transformation.
APIs (Application Programming Interfaces) enable real-time or near-real-time data extraction from web services or SaaS platforms. RESTful APIs, like those from Salesforce or Stripe, return JSON/XML data, which ETL pipelines process incrementally. For example, extracting customer data via an API ensures the latest records are available for analysis. APIs often require handling authentication (OAuth tokens), pagination, and rate limits. Streaming APIs (e.g., Twitter or IoT sensors) add complexity but support continuous data ingestion. Tools like Apache NiFi or custom scripts often manage API integrations to ensure reliability and scalability.
These three sources cover most ETL use cases, but hybrid approaches (e.g., combining database exports with API calls) are common as systems evolve.