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Import Slack Data Using Azure Data Factory



Use CData Connect Cloud to connect to Slack Data from Azure Data Factory and import live Slack data.

Microsoft Azure Data Factory (ADF)) is a completely managed, serverless data integration service. When combined with CData Connect Cloud, ADF enables immediate cloud-to-cloud access to Slack data within data flows. This article outlines the process of connecting to Slack through Connect Cloud and accessing Slack data within ADF.

CData Connect Cloud offers a cloud-to-cloud interface tailored for Slack, granting you the ability to access live data from Slack data within Azure Data Factory without the need for data replication to a natively supported database. Equipped with optimized data processing capabilities by default, CData Connect Cloud seamlessly channels all supported SQL operations, including filters and JOINs, directly to Slack. This harnesses server-side processing to expedite the retrieval of the desired Slack data.

Configure Slack Connectivity for ADF

Connectivity to Slack from Azure Data Factory is made possible through CData Connect Cloud. To work with Slack data from Azure Data Factory, we start by creating and configuring a Slack connection.

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

  1. Log into Connect Cloud, click Connections and click Add Connection
  2. Select "Slack" from the Add Connection panel
  3. Enter the necessary authentication properties to connect to Slack. Slack uses the OAuth authentication standard. To authenticate using OAuth, you will need to create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the Getting Started section of the help documentation for an authentication guide.
  4. Click Create & Test
  5. Navigate to the Permissions tab in the Add Slack 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 Slack data from Azure Data Factory.

Access Live Slack Data in Azure Data Factory

To establish a connection from Azure Data Factory to the CData Connect Cloud Virtual SQL Server API, follow these steps.

  1. Login to Azure Data Factory.
  2. If you have not yet created a Data Factory, Click New -> Dataset.
  3. In the search bar, enter SQL Server and select it when it appears. On the following screen, enter a name for the server. In the Linked service field, select New.
  4. Enter the connection settings.
    • Name - enter a name of your choice.
    • Server name - enter the Virtual SQL Server endpoint and port separated by a comma: tds.cdata.com,14333
    • Database name - enter the Connection Name of the CData Connect Cloud data source you want to connect to (for example, Slack1).
    • User Name - enter your CData Connect Cloud username. This is displayed in the top-right corner of the CData Connect Cloud interface. For example, [email protected].
    • Password - select Password (not Azure Key Vault) and enter the PAT you generated on the Settings page.
    • Click Create.
  5. In Set properties, set the Name, choose the Linked service we just created, select a Table name from those available, and Import schema from connection/store. Click OK.
  6. After creating the linked service, the following screen should appear:
  7. Click preview data to see the imported Slack table.
  8. You can now use this dataset when creating data flows in Azure Data Factory.

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