Post by account_disabled on Dec 14, 2023 5:23:26 GMT
Let us take a look at the Azure Data Factory section to see what services we can use. So that we can link the “Activities” that we setup earlier to work in the order that we set. In this part, we can connect (Chain) the pipeline we want with the “parent pipeline” and other triggers as well Let's come back to the Data Engineer project we created with our Data Factory. Now we have 8 pipelines, 17 datasets, and 3 data flows to make it easier to work with. We will group our pipelines into Folders based on the purpose of the pipeline, such as ingestion, processing, and SQL folders. We will also group datasets of the same type into folders.
including raw data, process, and SQL dataset Whatsapp Number List folders, as shown below. Grouping pipeline and dataset into folders After that, we will create a parent pipeline by creating a “parent pipeline” and combine the Ingestion and Processing pipelines with that parent pipeline. Then let us do “Publish” and it will be finished for our Data Engineer Panaya Sutta Last but not Least… Let us take a look at the Azure Data Factory section to see what services we can use. So that we can link the “Activities” that we setup earlier to work in the order that we set. In this part, we can connect (Chain) the pipeline we want with the “parent pipeline” and other triggers as well Let's come back to the Data Engineer project we created with our Data Factory.
Now we have 8 pipelines, 17 datasets, and 3 data flows to make it easier to work with. We will group our pipelines into Folders based on the purpose of the pipeline, such as ingestion, processing, and SQL folders. We will also group datasets of the same type into folders, including raw data, process, and SQL dataset folders, as shown below. Grouping pipeline and dataset into folders After that, we will create a parent pipeline by creating a “parent pipeline” and combine the Ingestion and Processing pipelines with that parent pipeline. Then let us do “Publish” and it will be finished for our Data Engineer Panaya Sutta Last but not Least.
including raw data, process, and SQL dataset Whatsapp Number List folders, as shown below. Grouping pipeline and dataset into folders After that, we will create a parent pipeline by creating a “parent pipeline” and combine the Ingestion and Processing pipelines with that parent pipeline. Then let us do “Publish” and it will be finished for our Data Engineer Panaya Sutta Last but not Least… Let us take a look at the Azure Data Factory section to see what services we can use. So that we can link the “Activities” that we setup earlier to work in the order that we set. In this part, we can connect (Chain) the pipeline we want with the “parent pipeline” and other triggers as well Let's come back to the Data Engineer project we created with our Data Factory.
Now we have 8 pipelines, 17 datasets, and 3 data flows to make it easier to work with. We will group our pipelines into Folders based on the purpose of the pipeline, such as ingestion, processing, and SQL folders. We will also group datasets of the same type into folders, including raw data, process, and SQL dataset folders, as shown below. Grouping pipeline and dataset into folders After that, we will create a parent pipeline by creating a “parent pipeline” and combine the Ingestion and Processing pipelines with that parent pipeline. Then let us do “Publish” and it will be finished for our Data Engineer Panaya Sutta Last but not Least.