Use the CData JDBC Driver for Azure Data Lake Storage in MicroStrategy Web

MicroStrategy is an analytics and mobility platform that enables data-driven innovation. When you pair MicroStrategy with the CData JDBC Driver for Azure Data Lake Storage, you gain database-like access to live Azure Data Lake Storage data from MicroStrategy, expanding your reporting and analytics capabilities. In this article, we walk through adding Azure Data Lake Storage as an external data source in MicroStrategy Web and creating a simple visualization of Azure Data Lake Storage data.

The CData JDBC driver offers unmatched performance for interacting with live Azure Data Lake Storage data in MicroStrategy due to optimized data processing built into the driver. When you issue complex SQL queries from MicroStrategy to Azure Data Lake Storage, the driver pushes supported SQL operations, like filters and aggregations, directly to Azure Data Lake Storage and utilizes the embedded SQL engine to process unsupported operations (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can visualize and analyze Azure Data Lake Storage data using native MicroStrategy data types.

Connect to and Visualize Azure Data Lake Storage Data using MicroStrategy Web

You can connect to Azure Data Lake Storage in MicroStrategy Web by adding a data source based on the CData JDBC Driver for Azure Data Lake Storage.^* Before you begin, you will need install the JDBC Driver for Azure Data Lake Storage on the machine hosting the MicroStrategy Intelligence Server that your instance of MicroStrategy Web is connected to. Once you have created a data source you can build dynamic visualizations of Azure Data Lake Storage data in MicroStrategy Web.

1. Open MicroStrategy Web and select your project.
2. Click Add External Data, select Databases, and use Select Tables as the Import Option.
3. In the Import from Tables wizard, click to add a new Data Source.
4. Select Generic in the Database menu and select Generic DBMS in the Version menu.
5. Click the link to show the connection string and opt to edit the connection string. In the Driver menu, select MicroStrategy Cassandra ODBC Driver (MicroStrategy requires a certified driver to interface through JDBC, the actual driver will not be used).
6. Set the connection string to the following: JDBC;MSTR_JDBC_JAR_FOLDER=PATH\TO\JAR\;DRIVER=cdata.jdbc.adls.ADLSDriver;URL={jdbc:adls:Schema=ADLSGen2;Account=myAccount;FileSystem=myFileSystem;AccessKey=myAccessKey;};

   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.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Azure Data Lake Storage JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.adls.jar

   Fill in the connection properties and copy the connection string to the clipboard.

   When you configure the JDBC URL, you may also want to set the Max Rows connection property. This will limit the number of rows returned, which is especially helpful for improving performance when designing reports and visualizations.

7. Right-click on the new data source, and choose Edit catalog options.
8. Edit the SQL Statement to SELECT * FROM SYS_SCHEMAS to read the metadata from the JDBC Driver.
9. Select the new data source to view the available tables. You may need to manually click the search icon in the Available Tables section to see the tables.
10. Drag tables into the pane to import them. Note: Since we create a live connection, we can import whole tables and utilize the filtering and aggregation features native to the MicroStrategy products to customize our datasets.
11. Click Finish, choose to the option to connect live, save the query, and choose the option to create a new dossier. Live connections are possible and effective, thanks to high-performance data processing native to CData JDBC drivers.
12. Choose a visualization, choose fields to display and apply any filters to create a new visualization of Azure Data Lake Storage data. Data types are discovered automatically through dynamic metadata discovery. Where possible, the complex queries generated by the filters and aggregations will be pushed down to Azure Data Lake Storage, while any unsupported operations (which can include SQL functions and JOIN operations) will be managed client-side by the CData SQL engine embedded in the driver.
13. Once you have finished configuring the dossier, click File -> Save.

Using the CData JDBC Driver for Azure Data Lake Storage in MicroStrategy Web, you can easily create robust visualizations and reports on Azure Data Lake Storage data. Read our other articles on connecting to Azure Data Lake Storage in MicroStrategy and connecting to Azure Data Lake Storage in MicroStrategy Desktop for more examples.

Note: Connecting using a JDBC Driver requires a 3- or 4-Tier Architecture.