Data Extraction

In ETL (Extract, Transform, Load) process, data extraction is the first and most critical step. It involves retrieving data from source systems, such as databases, files, or APIs and preparing it for transformation. The extracted data is then stored in a staging area, a temporary space where it can be cleaned, validated, and processed before being moved to the target system.

Data extraction methods vary based on the nature of the Datasource, the frequency of updates, and the business use case. These methods include Full Extraction, Incremental Extraction, and Query Extraction. It helps business ensure that the extracted data is accurate, relevant, and suited to the requirements.

Full Extraction

In this method, the entire dataset is extracted from the source, regardless of any previous extraction. This approach is ideal for scenarios where you need to obtain a complete snapshot of the data for analysis or processing. This approach is simple and makes sure no records are left out. It works best for datasets that don’t change much over time. Full extraction is great for ETL because it provides a complete and reliable dataset for processing and loading, especially when setting up the system for the first time.

Example

When a bank integrates its loan database for the first time, full extraction is used to pull all historical records. This enables comprehensive analyses like loan portfolio performance tracking.

Incremental Extraction

This method retrieves only the new or updated records since the last extraction, minimizing data transfer and processing overhead. Incremental extraction is particularly useful for dynamic data sources where updates occur frequently. By extracting only what is necessary, incremental extraction saves resources while keeping the dataset current. Typical use cases include periodic updates for transactional systems, synchronizing data across systems, and building incremental data pipelines to maintain efficiency.

Note

Daily incremental extractions allow the bank to monitor dynamic metrics, such as the number of new loan approvals and repayment trends updates. A script might run nightly to fetch only loans approved or updated since the last extraction, keeping dashboards current without overburdening systems.

Query Extraction

Query extraction allows to retrieve specific subsets of data based on predefined conditions or queries. It is highly efficient for targeted data needs and helps optimize data processing by extracting only relevant records. This method optimizes performance by reducing data transfer and storage costs and ensures that only required data is extracted. Common use cases for filtered extraction include generating reports for a specific time period, extracting data for a particular region or segment, and isolating high-priority records for compliance or decision-making.

Example

A bank might extract data on loans overdue by more than 30 days for regulatory reporting using a query with condition like WHERE status = ‘overdue’ AND overdue_days >30. This allows precise targeting of data, ensuring only the relevant subset—such as overdue loans with a delay of more than 30 days—is retrieved.