Prerequisites

For your data ingest source

• You have your ADLS container, storage account, and path from which you want to ingest data.

• You have performed one of the following to configure access to the ADLS folders:

  • You have configured access to the ADLS folders with a RAZ enabled environment.

    It is a best practice to enable RAZ to control access to your object store folders. This allows you to use your Cloudera Public Cloud credentials to access ADLS folders, increases auditability, and makes object store data ingest workflows portable across cloud providers.

    1. Ensure that Fine-grained access control is enabled for your Cloudera DataFlow environment.
    2. From the Ranger UI, navigate to the ADLS repository.
    3. Create a policy to govern access to the ADLS container and path used in your ingest workflow. For example: adls-to-adls-avro-ingest
    4. Add the machine user that you have created for your ingest workflow to ingest the policy you just created.

    For more information, see Ranger policies for RAZ-enabled Azure environment.

  • You have configured access to ADLS folders using ID Broker mapping.

    If your environment is not RAZ-enabled, you can configure access to ADLS folders using ID Broker mapping.

    1. Access IDBroker mappings.
       1. To access IDBroker mappings in your environment, click Actions > Manage Access.
       2. Choose the IDBroker Mappings tab where you can provide mappings for users or groups and click Edit.
    2. Add your Cloudera Workload User and the corresponding Azure role that provides write access to your folder in ADLS to the Current Mappings section by clicking the blue + sign.
    3. Click Save and Sync.

For Cloudera DataFlow

• You have enabled Cloudera DataFlow for an environment.

  For information on how to enable Cloudera DataFlow for an environment, see Enabling Cloudera DataFlow for an Environment.

• You have created a Machine User to use as the Cloudera Workload User.

• You have given the Cloudera Workload User the EnvironmentUser role.
  1. From the Management Console, go to the environment for which Cloudera DataFlow is enabled.
  2. From the Actions drop down, click Manage Access.
  3. Identify the user you want to use as a Workload User.
  4. Give that user EnvironmentUser role.

• You have synchronized your user to the Cloudera Public Cloud environment that you enabled for Cloudera DataFlow.

  For information on how to synchronize your user to FreeIPA, see Performing User Sync.

• You have granted your Cloudera user the DFCatalogAdmin and DFFlowAdmin roles to enable your user to add the ReadyFlow to the Catalog and deploy the flow definition.
  1. Give a user permission to add the ReadyFlow to the Catalog.
     1. From the Management Console, click User Management.
     2. Enter the name of the user or group you wish to authorize in the Search field.
     3. Select the user or group from the list that displays.
     4. Click Roles > Update Roles.
     5. From Update Roles, select DFCatalogAdmin and click Update.
  2. Give your user or group permission to deploy flow definitions.
     1. From the Management Console, click Environments to display the Environment List page.
     2. Select the environment to which you want your user or group to deploy flow definitions.
     3. Click Actions > Manage Access to display the Environment Access page.
     4. Enter the name of your user or group you wish to authorize in the Search field.
     5. Select your user or group and click Update Roles.
     6. Select DFFlowAdmin from the list of roles.
     7. Click Update Roles.
  3. Give your user or group access to the Project where the ReadyFlow will be deployed.
     1. Go to DataFlow > Projects.
     2. Select the project where you want to manage access rights and click More > Manage Access.
  4. Start typing the name of the user or group you want to add and select them from the list.
  5. Select the Resource Roles you want to grant.
  6. Click Update Roles.
  7. Click Synchronize Users.

For your data ingest target

• You have your OpenAI API key.
• You have your Pinecone environment and index into which you want to ingest data.
• Your Pinecone index was configured with the ’text-embedding-ada-002 model’.
• You have your Pinecone API key.