On the Settings tab, choose the dataset associated with the Azure Table. The child pipeline is the one that we’ve been working on during previous episodes.Ĭreate a new pipeline and add the Lookup activity. To read the metadata table, we will create another pipeline that will fetch information about OData services to process and then, one by one, it will trigger the child pipeline responsible for the extraction. Create the Linked Service and a dataset as we did in previously in this series. There is a dedicated connector that allows us to consume data from Azure Table. To access the metadata table from the pipeline we have to create resources in Synapse Studio. I add two previously used OData services to the table. In the pipeline, we’ve defined three parameters so we have to create an additional property for the Host information. In the PartitionKey I store the OData service name and the RowID keeps the Entity name. By default, each Azure Table has two properties: PartitionKey and RowID, that together form the primary key.
#Easy mark plus printer plus#
Select the table that you’ve just created and click the plus button to add an entry. You can use Storage Explorer to add entries to the table. Click the plus button, provide the table name and click OK to confirm. To create a Table in Azure Storage, open the Storage Account blade in Azure Portal and choose Tables from the menu. And, as we store small amounts of data, the cost will be minimal. It can be part of the same storage account that we use for data lake, it’s simple to deploy, and it doesn’t require any maintenance. Instead, use Azure Table Storage which seems to offer exactly the functionality we need.
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We could use a SQL database, which meets many of our goals, but it is quite a heavy service. To store information about OData services, we need a service, that is easy to provision and maintain.
#Easy mark plus printer code#
There is a GitHub repository with source code for each episode. Let’s further enhance our pipeline and make it even more agile! Not the most effective approach.īut what if we could provide all OData services upfront in an external datastore? That’s the plan for today. If we want to extract data from many services, we have to start the pipeline multiple times, each time providing the OData service name, entity and host. The extraction job can only extract a single OData service at a time, and we still have to provide parameter values manually. It was a great improvement, but the process still has two main disadvantages. It allows us to change the OData service we want to use without modifying the pipeline or resources. Then, a week later, we enhanced the design to support parameters, which eliminated some of the hardcoded values. In the first episode, we’ve built a simple pipeline that extracts data from a selected OData service and saves it to the data lake.
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#Easy mark plus printer how to#
Welcome to the third episode of this mini blog series, where I show you how to deal with OData extraction from the SAP system using Synapse Pipelines. Before implementing data extraction from SAP systems please always verify your licensing agreement.