Azure Data Factory is the primary task orchestration and data transformation service on the Azure cloud. This means that Azure Data Factory can be used for connecting to various Azure Cloud as well as external services to perform automated tasks. To make it easier for users to create pipelines corresponding to various scenarios, Azure Data Factory comes with pre-built templates, which can be accessed using the Template gallery:
The available templates can be broadly classified into the following categories:
- Copy Templates: The primary use case for any ETL tool or service is to move data from one source to another. This concept applies to Azure Data Factory as well. Due to this reason, there are multiple Data Factory templates available in the Template gallery which cater to this functionality. Some of them accomplish tasks such as bulk load from database, move files, copy from <source> to <destination>
- SSIS Templates: These templates provide the functionality of scheduling execution of SSIS packages using the Azure-Integration Runtime.
- Transform Templates: These templates are designed to accomplish end-to-end data transformation e.g. Validating the source data from source, copying to the intermediate destination, transforming and loading the data to the final destination (all within one pipeline).
Besides, Azure Data Factory also provides the ability to create custom templates. This can be accomplished by using the Save as template option in the Data Factory GUI.
The custom templates are available in the My Templates section. An important point to note is that GIT integration should be enabled in order to use the My Templates feature.
Reference: https://docs.microsoft.com/en-us/azure/data-factory/solution-templates-introduction
Azure Data Engineering 