Extract-Load (EL) Process – Detailed Description

1. Extract

• Data is extracted from various source systems (e.g., databases, APIs, files).
• Data can be in different formats and structures (e.g., relational, JSON, XML, flat files).
• Extraction methods include:
  • Update Notification: The source system notifies about changes, which are then extracted.
  • Incremental Extraction: Only new or changed data is extracted.
  • Full Extraction: All data is extracted every time.
• Extracted data is often temporarily stored in a staging area.

2. Load

• The extracted data is loaded into a target system (e.g., data warehouse, data lake).
• Loading methods include:
  • Full Load: All data is imported completely.
  • Incremental Load: Only new or changed data is loaded.
  • Streaming Load: Continuous loading of small data amounts in real time.
  • Batch Load: Periodic loading of large data sets.
• Data validation often takes place during extraction and loading.

Special Features of the EL Process

• Unlike ETL, data is usually loaded raw and unchanged.
• Transformations are performed later in the target system (ELT approach).
• Flexibility and scalability are key advantages.

When is the EL process preferred over ETL?

• Large Data Volumes and Performance: Raw data can be loaded faster; transformation happens later.
• Modern Cloud Architectures: Powerful data warehouses (e.g., Snowflake, BigQuery) handle transformations.
• Flexibility: Different user groups can apply various transformations to the raw data as needed.
• Real-Time and Streaming Requirements: Direct loading minimizes latency and enables near real-time processing.
• Minimizing Latency: Data is available faster; transformation can be performed later.