What are common data sources for ETL extraction (e.g., relational databases, flat files, APIs)?

Common data sources for ETL (Extract, Transform, Load) extraction include relational databases, flat files, and APIs. These sources are widely used because they cover structured, semi-structured, and unstructured data, which are foundational to most data integration workflows. Relational databases like MySQL, PostgreSQL, or Microsoft SQL Server store data in tables with predefined schemas, making them straightforward to query using SQL. Flat files such as CSV, JSON, or Excel spreadsheets are simple to transport and process but may require validation for formatting or encoding issues. APIs, especially RESTful web services, provide access to real-time or near-real-time data from applications like Salesforce or payment gateways, often returning JSON or XML responses that need parsing.

Beyond these core sources, NoSQL databases and cloud storage systems are increasingly common. NoSQL databases like MongoDB or Cassandra handle unstructured or semi-structured data, which can be extracted using database-specific drivers or connectors. Cloud storage platforms such as Amazon S3 or Google Cloud Storage store large volumes of files (e.g., logs, backups) that ETL processes can batch process. Streaming data from tools like Apache Kafka or AWS Kinesis is another category, enabling real-time extraction for use cases like monitoring or analytics. These sources often require additional configuration, such as handling authentication for cloud services or managing data partitioning for scalability.

Specialized systems and applications also serve as data sources. For example, enterprise resource planning (ERP) systems like SAP or legacy mainframes may require custom connectors due to proprietary data formats. Log files from servers or applications, which record events or errors, are typically unstructured and need parsing with regular expressions or log-specific tools. SaaS platforms like HubSpot or Zendesk often expose APIs with rate limits or pagination, requiring careful handling to avoid throttling. Developers must also consider data volume, latency, and security (e.g., encryption for sensitive data) when designing extraction logic, as these factors influence tool choices (e.g., Apache NiFi for file-based workflows or Airflow for API orchestration).