Create Reports from Azure Data Lake Storage Data in Google Data Studio

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CData Connect



Use CData Connect Cloud to create a virtual MySQL Database for Azure Data Lake Storage data and create custom reports in Google Data Studio.

Google Data Studio allows you to create branded reports with data visualizations to share with your clients. When paired with CData Connect Cloud, you get instant, 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 and build reports from Azure Data Lake Storage data in Google Data Studio.

CData Connect Cloud provides a pure cloud-to-cloud interface for Azure Data Lake Storage, allowing you to easily build reports from live Azure Data Lake Storage data in Google Data Studio — without replicating the data. As you build visualizations, Google Data Studio generates queries to gather data. Using optimized data processing out of the box, CData Connect Cloud pushes all supported query operations (filters, JOINs, etc) directly to Azure Data Lake Storage, leveraging server-side processing to quickly return Azure Data Lake Storage data.

This article requires a CData Connect Cloud instance and the CData Connect Cloud Connector for Google Data Studio. Get more information on the CData Connect Cloud and sign up for a free trial at https://www.cdata.com/connect.


Connect to Azure Data Lake Storage from Connect Cloud

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

  1. Log into 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 connect to Azure Data Lake Storage data from Google Data Studio.

Visualize Live Azure Data Lake Storage Data in Google Data Studio

The steps below outline connecting to CData Connect Cloud from Google Data Studio to create a new Azure Data Lake Storage data source and build a simple visualization from the data.

  1. Log into Google Data Studio, click data sources, create a new data source, and choose CData Connect Cloud Connector.
  2. Authorize the Connector to connect to an external service (your Connect Cloud instance).
  3. Use your instance name (myinstance in myinstance.cdatacloud.net), username, and password to connect to your Connect Cloud instance.
    • Username: myinstance/username
    • Password: your Connect Cloud password
  4. Select a Database (e.g. ADLS1) and click Next.
  5. Select a Table (e.g. Resources) and click Next.
  6. Click Connect.
  7. If needed, modify columns, click Create Report, and add the data source to the report.
  8. Select a visualization style and add it to the report.
  9. Select Dimensions and Measures to customize your visualization.

Optional: Connect with the MySQL Connector

If you need to work with data from a custom SQL query, you can use the MySQL Connector. Connect using the server information for your Connect Cloud instance (server address, port, username, and password).

SQL Access to Azure Data Lake Storage Data from Cloud Applications

Now you have a direct, cloud-to-cloud connection to live Azure Data Lake Storage data from your Google Data Studio workbook. You can create more data sources and new visualizations, build reports, and more — all without replicating Azure Data Lake Storage data.

Try CData Connect Cloud and get SQL data access to 200+ SaaS, Big Data, and NoSQL sources directly from your cloud applications.