Data Loading

In the ETL (Extract, Transform, Load) process, loading is the third step that transfers processed data from the staging area onto the target system, such as a data warehouse. This step ensures that the transformed data is available for analysis.

The various types of data uploads are - incremental, truncate and reload, update - each tailored to meet specific use cases for managing and integrating data.

Incremental Upload

Incremental upload adds new data to the existing dataset without changing any current records. It works well for situations where data keeps growing over time, like tracking ongoing transactions, log entries, or time-series data. This method keeps historical data intact, maintains a continuous record of changes, and is an efficient way to handle ever-expanding datasets.

Truncate and Reload

Truncate and Reload completely replaces the existing dataset with new data. This method is perfect when accuracy and consistency are essential, such as for master or reference data. It deletes all current records and loads updated data to ensure the dataset is always accurate and up-to-date. This approach is especially useful for static datasets that need regular full updates.

Update

The update method checks new data against existing records using a unique key column. If a match is found and any changes are detected, the matching record is updated with the new information. If no match exists, the new data is added as a new record. This method is ideal for master datasets that need regular synchronization or merging of data from multiple sources, ensuring dynamic updates and keeping information current.

During the ETL process, some records may not meet the required rules or criteria, leading to exceptions. In the next section, we will explore what these exceptions are and how they are handled to ensure the data remains accurate and reliable.