Integrate Live Azure Data Lake Storage Data into Amazon SageMaker Canvas with RDS

Amazon SageMaker Canvas is a no-code machine learning platform that lets you generate predictions, prepare data, and build models without writing code. When paired with CData Connect Cloud, you get instant, cloud-to-cloud access to Azure Data Lake Storage data for building custom machine-learning models, predicting customer churn, generating texts, building chatbots, and more. This article shows how to connect to Connect Cloud from Amazon SageMaker Canvas using the RDS connector and integrate live Azure Data Lake Storage data into your ML model deployments.

CData Connect Cloud provides a pure SQL, cloud-to-cloud interface for Azure Data Lake Storage, allowing you to easily integrate with live Azure Data Lake Storage data in Amazon SageMaker Canvas — without replicating the data. CData Connect Cloud looks exactly like a SQL Server database to Amazon SageMaker Canvas and uses optimized data processing out of the box to push all supported SQL operations (filters, JOINs, etc) directly to Azure Data Lake Storage, leveraging server-side processing to quickly return Azure Data Lake Storage data.

Configure Azure Data Lake Storage Connectivity for Amazon SageMaker Canvas

Connectivity to Azure Data Lake Storage from Amazon SageMaker Canvas is made possible through CData Connect Cloud. To work with Azure Data Lake Storage data from Amazon SageMaker Canvas, we start by creating and configuring a Azure Data Lake Storage connection.

1. Log into Connect Cloud, click Connections, and click Add Connection.
2. Select "Azure Data Lake Storage" from the Add Connection panel.
3. Enter the necessary authentication properties to connect to Azure Data Lake Storage.

   Authenticating to a Gen 1 DataLakeStore Account

   Gen 1 uses OAuth 2.0 in Azure AD for authentication.

   For this, an Active Directory web application is required. You can create one as follows:

   1. Sign in to your Azure Account through the .
   2. Select "Azure Active Directory".
   3. Select "App registrations".
   4. Select "New application registration".
   5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
   6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
   7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

   To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen1.
   • Account: Set this to the name of the account.
   • OAuthClientId: Set this to the application Id of the app you created.
   • OAuthClientSecret: Set this to the key generated for the app you created.
   • TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

   Authenticating to a Gen 2 DataLakeStore Account

   To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen2.
   • Account: Set this to the name of the account.
   • FileSystem: Set this to the file system which will be used for this account.
   • AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.
4. Click Create & Test.
5. Navigate to the Permissions tab in the Add Azure Data Lake Storage Connection page and update the User-based permissions.

Add a Personal Access Token

If you are connecting from a service, application, platform, or framework that does not support OAuth authentication, you can create a Personal Access Token (PAT) to use for authentication. Best practices would dictate that you create a separate PAT for each service, to maintain granularity of access.

1. Click on your username at the top right of the Connect Cloud app and click User Profile.
2. On the User Profile page, scroll down to the Personal Access Tokens section and click Create PAT.
3. Give your PAT a name and click Create.
4. The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured, you are ready to connect to Azure Data Lake Storage data from Amazon SageMaker Canvas.

Connecting to CData Connect Cloud from Amazon SageMaker Canvas

With the connection in CData Connect Cloud configured, you are ready to integrate live Azure Data Lake Storage data into Amazon SageMaker Canvas using its RDS connector.

1. Select a domain and user profile in Amazon SageMaker Canvas and click on "Open Canvas".
2. Once the Canvas application opens, navigate to the left panel, and select "My models".
3. Click on "Create new model" in the My models screen.
4. Specify a Model name in Create new model window and select a Problem type. Click on "Create".
5. Once the model version gets created, click on "Create dataset" in the Select dataset tab.
6. In the Create a tabular dataset window, add a "Dataset name" and click on "Create".
7. Click on the "Data Source" drop-down and search for or navigate to the RDS connector and click on " Add Connection".
8. In the Add a new RDS connection window, set the following properties:
   
   
   • Connection Name: a relevant connection name
   • Set Engine type to sqlserver-web
   • Set Port to 14333
   • Set Address as tds.cdata.com
   • Set Username to a Connect Cloud user (e.g. user@mydomain.com)
   • Set Password to the PAT for the above user
   • Set Database name the Azure Data Lake Storage connection (e.g., ADLS1)
9. Click on "Create connection".

Integrating Azure Data Lake Storage Data into Amazon SageMaker Canvas

With the connection to Connect Cloud configured in the RDS, you are ready to integrate live Azure Data Lake Storage data into your Amazon SageMaker Canvas dataset.

1. In the tabular dataset created in RDS with Azure Data Lake Storage data, search for the Azure Data Lake Storage connection configured on Connect Cloud in the search bar or from the list of connections.
2. Select the table of your choice from Azure Data Lake Storage, drag and drop it into the canvas on the right.
3. You can create workflows by joining any number of tables from the Azure Data Lake Storage connection (as shown below). Click on "Create dataset".
4. Once the dataset is created, click on "Select dataset" to build your model.
5. Perform analysis, generate prediction, and deploy the model.

At this point, you have access to live Azure Data Lake Storage data in Amazon SageMaker that you can utilize to build custom ML models to generate predictive business insights and grow your organization.

SQL Access to Azure Data Lake Storage Data from Cloud Applications

Now you have a direct connection to live Azure Data Lake Storage data from Amazon SageMaker Canvas. You can create more connections, datasets, and predictive models to drive business — all without replicating Azure Data Lake Storage data.

To get real-time data access to 100+ SaaS, Big Data, and NoSQL sources directly from your cloud applications, see the CData Connect Cloud.