This tutorial focuses on creating a data flow and a data factory. The first step is to go to the Azure Portal and create a new resource group. Once you have the group created, you can create a new data factory resource. The next step is to configure the data flow and data factory.
Configuring a data factory
In the Azure portal, navigate to Data Factories. Once there, click the Add button to create a data factory. Alternatively, you can navigate to a data factory’s page and use the search bar to find the resource you need. Once you have found the data factory, you can configure its settings.
Next, you will need to configure the Azure Data Factory’s permissions. First, you must create a New Client Secret and click “Save”. After saving the secret, you will see a page where you can specify the permissions for your Data Factory. Typically, you will need only the minimum permissions for your data factory. However, in some cases, you may need to fine-tune permissions.
Besides providing ETL services, an Azure Data Factory also allows data import and processing. It can also invoke custom virtual machines and trigger webhook callouts. Then, the data factory can consolidate and deliver the resulting data to a SQL database, a file system, or outbound FTP.
You should now connect to the Azure Data Factory. Depending on your data source, you can use a Source System or Stage Area connection. You can also use a Table-Based configuration for your Data Factory. If you need more help, you can check out Varigence’s YouTube channel for an introduction video. The landing connection video demonstrates how to configure an Azure data factory using MSSQL starting point metadata. It also shows how to connect to a staging area or a database.
Configuring a data flow
Configuring a data flow in Azure Data factory is a straightforward process. After creating a data flow, you can choose to ingest it into a data warehouse or a data store and then transfer that data to a second data store. The Data Factory uses JSON entities, and you can use your favorite text editor to create pipelines and input and output datasets.
Besides managing data flows, the Azure Data Factory provides other resources that need to be configured. The Azure Resource Manager can help you configure these resources. The Data Flow parameters are also available in the Azure Resource Manager. Once you’ve created your data flow, you can configure the rest of the resources using the corresponding parameters.
After creating your data flow, you can configure the transformation logic in it. You can also select a source transformation and add a transformation if necessary. Data flows are easy to maintain and can transform data without any coding. You can even implement the Slowly Changing Dimension using Mapping Data Flow.
Next, you can add additional data sources. You can add data from Blob Storage, Data Lake Storage Gen 1 and 2, SQL Database, Cosmos DB, and other data sources to your Azure Data Factory pipeline. You can also copy existing data and perform new transformations in Azure Data Factory.
Creating a data Flow
The first step in creating a data flow is to create an activity component. This activity component displays pipeline-related elements within the data factory. This activity can be customized to match your needs and preferences. You can also add annotations and parameters. You can configure the pipeline properties as per your needs.
You can create a data flow by using the Azure Data Factory. You can store data in an Azure SQL database and then create a data flow using the data in the database. The Data Factory can support data from other regions and computes. To use it, navigate to the Data Factory page and open the Data Factory UI. From there, you can start creating a Data Flow by selecting the components you want to use.
Creating a data flow in the data factory is an easy process. The first step involves creating a dataset and pipeline. You can use the Azure data factory to create a data flow and create an on-demand or bring your own computing environment. You can also choose to create the flow by using the Mapping Data Flow activity.
You can also use the data preview to verify your transformation configuration. Once you’ve created a Data flow, you can add transformations and apply filters to it. Once you’re ready, you can click the Finish button to finish the data flow.