Import CouchDB Data Using Azure Data Factory

Dibyendu Datta
Dibyendu Datta
Lead Technology Evangelist
Use CData Connect AI to connect to CouchDB Data from Azure Data Factory and import live CouchDB data.

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

CData Connect AI offers a cloud-to-cloud interface tailored for CouchDB, granting you the ability to access live data from CouchDB 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 AI seamlessly channels all supported SQL operations, including filters and JOINs, directly to CouchDB. This harnesses server-side processing to expedite the retrieval of the desired CouchDB data.

Configure CouchDB Connectivity for ADF

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

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

  1. Log into Connect AI, click Sources, and then click Add Connection
  2. Select "CouchDB" from the Add Connection panel
  3. Enter the necessary authentication properties to connect to CouchDB.

    Set the following to connect:

    • Url: The Url of your instance. For example: http://localhost:5984
    • User The Apache CouchDB user account used to authenticate.
    • Password The Apache CouchDB password associated with the authenticating user.
  4. Click Save & Test
  5. Navigate to the Permissions tab in the Add CouchDB Connection page and update the User-based 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.
  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 CouchDB data from Azure Data Factory.

Access Live CouchDB Data in Azure Data Factory

To establish a connection from Azure Data Factory to the CData Connect AI 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 AI data source you want to connect to (for example, ApacheCouchDB1).
    • User Name - enter your CData Connect AI username. This is displayed in the top-right corner of the CData Connect AI 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 CouchDB table.
  8. You can now use this dataset when creating data flows in Azure Data Factory.

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