Snowflake Online Training | Snowflake Online Course |

API Or Webhook-Based Incremental Loading:
If your data source provides APIs or web hooks, use them to fetch only the new or changed data since the last load.
This is common in scenarios involving external services or online platforms.
Choose the strategy that best fits your data and system architecture. Additionally, consider factors such as performance, data integrity, and the capabilities of the tools and platforms you are using for data loading. Always test your incremental loading process thoroughly to ensure accuracy and efficiency.

How Do You Incrementally Load Data Into A Snowflake With DBT?
In dbt (data build tool), you can incrementally load data into Snowflake using a technique called "incremental models." Incremental models allow you to update only the new or changed data in your destination (Snowflake) rather than reloading the entire dataset. Here's a general outline of the process:

Identify Incremental Key:
Identify a column or set of columns that can be used as an incremental key to determine which records are new or have been updated since the last run. This is typically a timestamp or a unique identifier for each record.

Create A Staging Table:
Create a staging table in Snowflake where you'll load the raw data. This table will hold the new and changed data from your source.

Create An Incremental Model:
Write a dbt model that selects data from the staging table and performs any necessary transformations. Use the incremental key identified earlier to filter only the new or changed record.