Connect to Azure Data Lake Storage Data in ACL Analytics

ACL Analytics, part of Diligent HighBond, is a powerful data analysis software primarily used for audit, risk management, and compliance. It enables professionals to examine and analyze large volumes of data to identify anomalies, trends, and potential risks or fraudulent activities.

CData Connect AI offers a dedicated cloud-to-cloud interface for Azure Data Lake Storage, enabling analytics directly from live Azure Data Lake Storage data within ACL Analytics, all without the need for data replication to a native database. With its inherent optimized data processing capabilities, CData Connect AI efficiently channels all supported SQL operations, including filters and JOINs, directly to Azure Data Lake Storage. This leverages server-side processing to swiftly deliver the requested Azure Data Lake Storage data.

Configure Azure Data Lake Storage Connectivity for ACL Analytics

Connectivity to Azure Data Lake Storage from ACL Analytics is made possible through CData Connect AI. To work with Azure Data Lake Storage data from ACL Analytics, we start by creating and configuring a Azure Data Lake Storage connection in CData Connect AI.

1. Log into Connect AI, click Sources, and then click Add Connection
👁 Adding a Connection

2. Select "Azure Data Lake Storage" from the Add Connection panel
👁 Selecting a data source

3. Enter the necessary authentication properties to connect to Azure Data Lake Storage. 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 Entra ID (formerly 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 "Entra ID" (formerly Azure AD).
   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.
   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Azure Data Lake Storage Connection page and update the User-based permissions. 👁 Updating permissions
   

Add a Personal Access Token

When connecting to Connect AI through the REST API, the OData API, or the Virtual SQL Server, a Personal Access Token (PAT) is used to authenticate the connection to Connect AI. It is best practice to create a separate PAT for each service to maintain granularity of access.

1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create. 👁 Creating a new PAT
   
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 and a PAT generated, you are ready to connect to Azure Data Lake Storage data from ACL Analytics.

Connect to Azure Data Lake Storage from ACL Analytics

The steps below outline connecting to CData Connect AI from ACL Analytics to create a new Azure Data Lake Storage data source. The CData Connect AI Virtual SQL Server allows you to establish a connection to your data from integration tools that support connections to SQL servers. The Virtual SQL Server mimics the behavior of a traditional SQL server, and it supports a range of query options.

1. With your Analytics File open, select 'Import' --> 'Database and application' 👁 Creating a new data source
   
2. Create a new connection
3. Set the connection information
   • Server: tds.cdata.com
   • Port: 14333
   • Auth Scheme: Password
   • Username: a Connect AI user, for example, [email protected]
   • Password: the PAT for the above Connect AI user
   • Database: the name of your Azure Data Lake Storage connection, for example, ADLS1
   👁 Connecting to Connect AI
   
4. Click "Test Connection"
5. Click "OK"
6. You are now ready to work with your Azure Data Lake Storage data in ACL Analytics! 👁 See your data in ACL Analytics
   

Live connections to Azure Data Lake Storage data from your applications

ACL Analytics can now connect to live Azure Data Lake Storage data directly through Connect AI, allowing you to analyze Azure Data Lake Storage data without duplicating it.

To get live data access to hundreds of SaaS, Big Data, and NoSQL sources directly from your applications, try CData Connect AI today!