In this lab, we will create an Azure Search service and a storage account. We recommend keeping both in a new and unique resource group, to make it easier to delete at the end of the workshop (if you want to). We will also upload the data to a blob storage within the storage account.
Step 1 - Create the Azure Search service
Go to the Azure portal and sign in with your Azure account.
Click Create a resource, search for Azure Search, and click Create. See Create an Azure Search service in the portal if you are setting up a search service for the first time.
For Resource group, create a resource group to contain all the resources you create in this tutorial. This makes it easier to clean up the resources after you have finished the tutorial.
For Location, choose either South Central US or West Europe. Currently, the preview is available only in these regions.
For Pricing tier, you can create a Free service to complete tutorials and quickstarts. For deeper investigation using your own data, create a paid service such as Basic or Standard. For these labs, we recommend using the Basic tier.
A Free service is limited to 3 indexes, 16 MB maximum blob size, and 2 minutes of indexing, which is often insufficient for exercising the full capabilities of cognitive search. To review limits for different tiers, see Service Limits.
[!NOTE] Cognitive Search is in public preview. Skillset execution is currently available in all tiers, including free. At a later time, the pricing for this capability will be announced.
- Pin the service to the dashboard for fast access to service information.
- After the service is created, collect the following information: URL from the Overview page, and api-key (either primary or secondary) from the Keys page. You will need them in the following labs.
Step 2 - Create the Azure Blob service and upload the data
The enrichment pipeline pulls from Azure data sources. Source data must originate from a supported data source type of an Azure Search indexer. For this exercise, we use blob storage to showcase multiple content types.
Download the sample data. The sample data consists of a small file set of different types.
Sign up for Azure Blob storage, create a storage account, log in to Storage Explorer, and create a container named
basicdemoin the same region as your Azure Search service created in the step above, to avoid latency between the search service and the files. You should use a general purpose account and LRS replication. For production environments, you may need to use another replication type. If you haven't done this before, you can refer to the Azure Storage Explorer Quickstart for instructions on all the steps.
Using Azure Storage Explorer, in the
basicdemocontainer you created, click Upload to upload the sample files. You can also upload the data from the Azure Portal.
After sample files are loaded, get the container name and a connection string for your Blob storage. You could do that by navigating to your storage account in the Azure portal. On Access keys, and then copy the Connection String field. We recommend storing the container name and connection string with your Azure Search URL and api-key from Step 1.
The connection string should be a URL similar to the following hypothetical example:
DefaultEndpointsProtocol=https;AccountName=cogsrchdemostorage;AccountKey=<your key here>==;EndpointSuffix=core.windows.net
There are other ways to specify the connection string, such as providing a shared access signature. We won't be covering that in this workshop, but to learn more about data source credentials, see Indexing Azure Blob Storage.