Build Interactive Dashboards from Azure Data Lake Storage Data in Amazon QuickSight

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CData Connect



Create a virtual MySQL database for Azure Data Lake Storage data in CData Connect Cloud and import Azure Data Lake Storage data into Amazon QuickSight SPICE to build interactive dashboards.

Amazon QuickSight allows users to build interactive dashboards in the cloud. When paired with CData Connect Cloud, you get cloud-to-cloud access to Azure Data Lake Storage data for visualizations, dashboards, and more. This article shows how to create a virtual database for Azure Data Lake Storage in Connect Cloud and build dashboards in Amazon QuickSight with access to Azure Data Lake Storage data.

CData Connect Cloud provides a pure MySQL, cloud-to-cloud interface for Azure Data Lake Storage, allowing you to easily build visualizations from Azure Data Lake Storage data in Amazon QuickSight. By importing your Azure Data Lake Storage data into the Amazon QuickSight "Super-fast, Parallel, In-memory Calculation Engine" (SPICE), you can leverage the powerful data processing features of the Amazon ecosystem to build responsive dashboards. And with the ability to schedule refreshes of the data stored in SPICE, you control how up-to-date your dashboards are.

Create a Virtual MySQL Database for Azure Data Lake Storage Data

CData Connect Cloud uses a straightforward, point-and-click interface to connect to data sources and generate APIs.

  1. Login to Connect Cloud and click Databases.
  2. Select "Azure Data Lake Storage" from Available Data Sources.
  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 Test Database.
  5. Click Privileges -> Add and add the new user (or an existing user) with the appropriate permissions.

With the virtual database created, you are ready to build visualizations in Amazon QuickSight.

Import Azure Data Lake Storage Data into SPICE and Create Interactive Dashboards

The steps below outline creating a new data set based on the virtual Azure Data Lake Storage database in Connect Cloud, importing the dataset into SPICE, and building a simple visualization from the data.

  1. Log into Amazon QuickSight and click "Manage data."
  2. Click "New data set," select MySQL as the data source, configure the connection to your Connect Cloud instance, and click "Create data source."
  3. Select a table to visualize (or submit a custom SQL query for your data).
  4. Click "Edit/Preview data" to customize the data set.
  5. Select "Import to SPICE for quicker analytics" and click "Visualize."
  6. Select fields to visualize and a visual type.

Schedule Refreshes for SPICE Data Sets

QuickSight users can schedule refreshes for data sets that are imported into SPICE, ensuring that data being analyzed is only as old as the most recent refresh.

  1. Navigate to the QuickSight home page.
  2. Click "Manage data."
  3. Select the data set you wish to refresh.
  4. Click "Schedule refresh."
  5. Click Create, configure the refresh settings (time zone, repeat frequency, and starting datetime), and click Create.

SQL Access to Azure Data Lake Storage Data from Cloud Applications

At this point, you have a direct, cloud-to-cloud connection to Azure Data Lake Storage data from your Amazon QuickSight dashboard. You can create new visualizations, build interactive dashboards, and more. For more information on gaining SQL access to data from more than 100 SaaS, Big Data, and NoSQL sources from cloud applications like Amazon QuickSight, refer to our Connect Cloud page.