As a service administrator, you can use an extract service remote agent to connect to your on-premises systems such as E-Business Suite, Peoplesoft, and JD Edwards, load data from these on-premises systems into Oracle NetSuite Analytics Warehouse, and then use the on-premises data to create data augmentations.

1. Perform Remote Agent Connection Prerequisites.
2. Configure TLS for Remote Agent Configuration (Optional).
3. Run the Remote Agent docker container and use the Remote Agent command line interface tool.
   1. For Windows users, see Run the Remote Agent Docker on Windows and Configure the Remote Agent Using the Command Line Interface Tool on Windows.
   2. For Linux users, see Run the Remote Agent Docker on Linux and Configure the Remote Agent Using the Command Line Interface Tool on Linux.
4. Create the Remote Agent Connection.

As a service administrator, you can use an extract service remote agent to connect to your on-premises Oracle E-Business Suite system.

1. Set up the remote agent to load data from your on-premises Oracle E-Business Suite system into Oracle NetSuite Analytics Warehouse.
   See Set up the Remote Agent to Load Data into NetSuite Analytics Warehouse (Preview).
2. Configure the remote agent and E-Business Suite data source on the Data Configuration page in Oracle NetSuite Analytics Warehouse using these instructions:
   1. On the Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type, and select Oracle E-Business Suite On-Prem as the connection type.
      
      
      
      
   5. In Create Connection for Oracle E-Business Suite On-Prem, select Remote as connectivity type.
      
      
      
      
   6. In the Remote Agent field, select the remote agent connection that you created, for example, EBS-Remote Agent. Enter an email address to receive notifications in Notification Email, provide the credentials in User Name and Password, the E-Business Suite connection using the JDBC format such as jdbc:oracle:thin:@<HOST>:<PORT>/<DB_NAME/SID_NAME> in URL, and select Application Object Library and General Ledger offerings mandatorily, and any other E-Business Suite offerings that you want to load data from in Offerings.
   7. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection. You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for E-Business Suite On-Prem unless you perform a metadata extract.
   8. Confirm that you see the Remote Agent and E-Business Suite connections on the Manage Connections page.
   9. Test both the connections by selecting the Test Connection option in Actions. You can check the statuses of all these requests on the Data Configuration Request History page.
   10. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

       Note:

       Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
3. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the E-Business Suite data. Select the applicable E-Business Suite source tables. See Augment Your Data.

As a service administrator, you can use an extract service remote agent to connect to your on-premises JD Edwards system and use the JD Edwards data to create data augmentations.

1. Set up the remote agent to load data from your on-premises Oracle JD Edwards system into Oracle NetSuite Analytics Warehouse.
   See Set up the Remote Agent to Load Data into NetSuite Analytics Warehouse (Preview).
2. Configure the remote agent and JD Edwards data source on the Data Configuration page in Oracle NetSuite Analytics Warehouse using these instructions:
   1. On the Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type, and then select Oracle JD Edwards On-Prem as the connection type.
      
      
      
      
   5. In Create Connection for Oracle JD Edwards On-Prem, in Connectivity Type, verify that Remote is selected automatically.
      
      
      
      
   6. In Remote Agent, select the remote agent connection that you created earlier, for example, Remote Agent.
   7. Enter an email address to receive notifications in Notification Email, provide credentials for your JD Edwards source in User Name and Password, and the URL of your JD Edwards source in URL.
   8. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection. You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for JD Edwards unless you perform a metadata extract.
   9. Confirm that you see the Remote Agent and JD Edwards connections on the Manage Connections page.
   10. Test both the connections by selecting the Test Connection option in Actions. You can check the statuses of all these requests on the Data Configuration Request History page.
   11. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

       Note:

       Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
3. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the JD Edwards data. Select the applicable JD Edwards source tables. Select the applicable JD Edwards source tables. See Augment Your Data.

As a service administrator, you can use an extract service remote agent to connect to your on-premises Oracle PeopleSoft system.

1. Set up the remote agent to load data from your on-premises Oracle E-Business Suite system into Oracle NetSuite Analytics Warehouse.
   See Set up the Remote Agent to Load Data into NetSuite Analytics Warehouse (Preview).
2. Configure the remote agent and PeopleSoft data source on the Data Configuration page in Oracle NetSuite Analytics Warehouse using these instructions:
   1. On the Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type, and then select the connection type based on the functional module that you want to connect to. For example, to connect to the "Financials" module, select Oracle PeopleSoft On-Prem - Financials as the connection type.
      
      
      
      
   5. In Create Connection for Oracle PeopleSoft On-Prem - Financials dialog, in Connectivity Type, verify that Remote is selected automatically.
      
      
      
      
   6. In Remote Agent, select the remote agent connection that you created earlier, for example, Remote Agent.
   7. Enter an email address to receive notifications in Notification Email, provide credentials for your PeopleSoft source in User Name and Password, and the URL of your PeopleSoft source in URL.
   8. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection. You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for PeopleSoft unless you perform a metadata extract.
   9. Click Save.
   10. On the Manage Connections page, select Actions for the PeopleSoft connection and then select Test Connection. You can check the statuses of all these requests on the Data Configuration Request History page.
   11. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

       Note:

       Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
3. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the PeopleSoft data. Select the applicable PeopleSoft source tables. See Augment Your Data.

As a service administrator, you can use an extract service remote agent to connect to your on-premises MySQL database.

1. Set up the remote agent to load data from your on-premises MySQL database into Oracle NetSuite Analytics Warehouse.
   See Set up the Remote Agent to Load Data into NetSuite Analytics Warehouse (Preview).
2. Check the port number for your on-premises MySQL database and create a service request with server host and port details to enable network connectivity to the on-premises MySQL server.
3. Specify the remote agent and configure the on-premises MySQL database on the Data Configuration page in Oracle NetSuite Analytics Warehouse by following these steps:
   1. On the Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type, and then select MySQL On-Prem as the connection type.
      
      
      
      
   5. In Connectivity Type, select Remote.
   6. In the Remote Agent field, select the remote agent connection that you created, for example, MySQL-Remote Agent. Enter an email address to receive notifications in Notification Email, and provide these details:
      • Host Name: Enter the host name of MySQL server such as 100.111.252.64
      • Port Number: Enter the port number where the server is listening such as 3306
      • Database: Enter the database name you need to connect to such as airportdb
      • Credentials to access the database in User Name and Password
      • Last Update Date Column Pattern: Enter "%r%a%o%%"

        Note:

        If column name matches with pattern provided, then NetSuite Analytics Warehouse marks the column name with isLastUpdateDate = true and uses it for the incremental extract. For example, if pattern provided is “%mo%fie%te%“, then the column name modifiedDate is marked as isLastUpdateDate = true.
      • Initial Extract Date Column Pattern: Enter "%e%art%new"

        Note:

        If column name matches with pattern provided, then NetSuite Analytics Warehouse marks the column name with isCreationDate = true and uses it for the initial extract date extraction. For example, if pattern provided is: “%cr%ted%te%“, then the column name createdDate is marked as isCreationDate = true.
      
      
      
      
      
   7. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection. You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for on-premises MySQL database unless you perform a metadata extract.
   8. Confirm that you see the Remote Agent and on-premises MySQL database connections on the Manage Connections page.
   9. Test both the connections by selecting the Test Connection option in Actions. You can check the statuses of all these requests on the Data Configuration Request History page.
   10. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

       Note:

       Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
4. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the on-premises MySQL database data. Select the applicable on-premises MySQL database source tables. See Augment Your Data.

As a service administrator, you can use the Oracle NetSuite Analytics Warehouse extract service to acquire data from SQL Server and use it to create data augmentations.

1. Set up the remote agent to load data from your SQL Server into Oracle NetSuite Analytics Warehouse.
   See Set up the Remote Agent to Load Data into NetSuite Analytics Warehouse (Preview).
2. In Oracle NetSuite Analytics Warehouse, create the SQL Server data connection using these instructions:
   1. In Oracle NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type, and then select SQL Server as the connection type.
      
      

      

      
      
   5. In the dialog for the SQL Server connection, enter these details and click Save:
      • Connectivity Type: Select Remote.
      • Remote Agent: Select the remote agent that you had set up to load data from your SQL Server.
      • Notification Email: Enter an email address to receive notifications.
      • SQL Server Name: Enter the SQL server name.
      • Port Number: Enter the port number on which your SQL server is available.
      • Database Name: Enter the database name in your SQLServer instance.
      • Schema Name: Enter the name of the schema for the dataset you want to load to run analytics on.
      • User Name and Password: Enter the credentials for your SQL Server instance.
      • Initial Extract Date Column Pattern: MM/dd/yyyy is the date format in your initial extract column; sample date is 1/23/1998.
      • Last Update Date Column Pattern: Last update date shows when the last update was done in your SQL Server database.

      
      

      

      
      
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for SQL Server unless you perform a metadata extract.
   7. Click Save.
3. On the Manage Connections page, select Actions for the SQL Server connection and then select Test Connection.
4. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
5. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the SQL Server data. Select the applicable SQL Server source tables. See Augment Your Data.

The CSV parser in the extract service for file extractors uses Opencsv. The csv files that are processed by extractservice must be compliant with the Opencsv standards.

See Opencsv File Standards. In addition to the CSV parser, the extract service supports files that are compliant with RFC4180 specification. The RFC 4180 CSV parser enables you to ingest single-line and multi-line data within your .csv files. The RFC 4180 parser supports ingesting data records with up to 99 line breaks. For more information on the RFC 4180 specification, see Common Format and MIME Type for Comma-Separated Values (CSV) Files.

Extractors such as Secure FTP (SFTP), Amazon Simple Storage Service (AWS S3), and Oracle Object Storage Service use CSV data files that have date and timestamp fields.

As a service administrator, you can use the Oracle NetSuite Analytics Warehouse extract service to acquire data from Amazon Simple Storage Service (AWS S3) and use it to create data augmentations.

1. In Oracle NetSuite Analytics Warehouse, create the AWS S3 data connection using these instructions:
   1. In Oracle NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type, and then select AWS S3 as the connection type.
      
      

      

      
      
   5. In the dialog for the AWS S3 connection, select Standard in Connectivity Type, enter an email address to receive notifications in Notification Email, and provide applicable details of your AWS S3.
      
      

      

      
      
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for AWS S3 unless you perform a metadata extract.
   7. Click Save.
2. In Oracle Object Storage Service:
   1. Create the folder structure in the Bucket using these guidelines:
      

      • The base folder in the bucket must match with the details provided in the connection.
      • Inside the base folder, ensure to place each file in its own folder.
      • Ensure that the Prefix of Data_Store_Name (same as Folder name) and Files in the target folder match exactly.

      See the "To create a folder or subfolder" section in Using the Console.

   2. Inside the base folder, create the metadata file for the Data Store List. This file lists the supported data stores. Each data store is a folder that has the actual file used in data augmentation, for example, ASSETS. Ensure that the file name and folder name match and there aren’t any special characters (including space) in the datastore, folder or file names.
      
   3. Create the metadata file for each data file under the data store folder using these guidelines:

      The META_DATASTORES.csv must have these columns:

      • DATA_STORE_NAME - A mandatory column to identify the data store name.
      • DATA_STORE_LABEL - A non-mandatory column that identifies the description of the data store.

      Note:

      If you add new datastores to an existing source connection, make sure that META_DATASTORES.csv contains the full list of datastores, including the older existing datastores.

      Each folder must have:

      • A data file that has the actual data that gets loaded into NetSuite Analytics Warehouse. This file must have a prefix with the DATA STORE NAME.
      • A metadata file for the list of columns contains all the column information on the data. This file must have a Prefix with META_DATASTORES_<DATA_STORE_NAME>_COL.
        • For the columns in this metadata, ensure the following:
        • If column name is ABC, then metadata can be ABC or “ABC” - the double quotes are ignored.
        • If column name is “ABC”, then metadata must be “”ABC”” – the first double quotes are ignored.

      Example

      In the image, the folder name is ACTIVITY_TYPES. Hence, the data store name is ACTIVITY_TYPES. You can confirm this from the META_DATASTORES.csv file. In this example, the file is named ACTIVITY_TYPES.xlsx or ACTIVITY_TYPES.csv. The metadata file must be META_DATASTORES_ACTIVITY_TYPES_COL.csv.

      The META_DATASTORES_ACTIVITY_TYPES_COL.csv has these columns:

      • DATA_STORE_NAME - This is a mandatory column.
      • COLUMN_NAME - This is a mandatory column.
      • COLUMN_LABEL - This is a non-mandatory column.
      • DATA_TYPE – This is a mandatory column.
      • WIDTH – This column identifies the string length.
      • PRECISION - This column value must be Numeric data type.
      • SCALE - This column value must be Numeric data type.
      • KEY_SEQUENCE - This is a mandatory column that identifies the Primary Key definition. If you’re using the composite primary key, then use column order numbers as values.
3. On the Manage Connections page, select Actions for the AWS S3 connection and then select Test Connection.
4. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
5. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the AWS S3 data. Select the applicable AWS S3 source tables. See Augment Your Data.

As a service administrator, you can use the NetSuite Analytics Warehouse extract service to acquire data from Oracle Object Storage Service and use it to create data augmentations.

1. Store the following details in a text file to use while creating the connection to Oracle Object Storage Service in NetSuite Analytics Warehouse:
   1. In Oracle Object Storage Service, create the Remote Host Extract Files directory as the base folder in which you must place all your data files. Note down the name of this directory. See the "To create a folder or subfolder" section in Using the Console.
   2. Obtain the URL of the Oracle Object Storage Service by signing into the Oracle Cloud Infrastructure Console and navigating to the bucket to get the details of the region, namespace, and bucket name. For example, the URL must be in the https://objectstorage.<region>.oraclecloud.com/n/<namespace>/b/<name of the bucket> format. See the "To view bucket details" section in Using the Console.
   3. Obtain a user’s OCID by navigating in the Oracle Cloud Infrastructure Console to Identity & Security, and then Users. On the Users page, search for a user who has access to the bucket used in the connector and copy the OCID. Obtain the tenancy ID by clicking your profile icon and then Tenancy in the Oracle Cloud Infrastructure Console. Under Tenancy information, copy the OCID. See Where to Get the Tenancy's OCID and User's OCID.
   4. Obtain the fingerprint for a user from the Oracle Cloud Infrastructure Console. Navigate to API Keys under Resources on the user page, and then click Add API Keys. In the Add API Keys dialog, ensure that Generate API Key Pair is selected. Download the private and public keys using the Download Private Key and Download Public Key options. You must copy the entire text of the private key along with the comments before and after the actual key. These comments could be as simple as: “---------------Begin RSA Private Key --------” and “-----------End of RSA Private Key----------“. Don’t copy only the alphanumeric key without the header and footer comments. In the Add API Keys dialog, select Choose Public Key File to upload your file, or Paste Public Key, if you prefer to paste it into a text box and then click Add. Copy the fingerprint that you see after you upload the public key in the Console. It looks something like this:12:34:56:78:90:ab:cd:ef:12:34:56:78:90:ab:cd:ef.
2. In NetSuite Analytics Warehouse, create the Oracle Object Storage connection using these instructions:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type, and then select Oracle Object Storage Service as the connection type.
      
   5. In the dialog for the Oracle Object Storage Service connection, select Standard in Connectivity Type and enter these details:
      • Connection Name: Object Storage
      • Connection Type: Standard
      • Notification Email: An email address to receive notifications
      • Remote Host Extract Files Directory: Name of the base folder in which you must place all your data files in Oracle Object Storage Service
      • URL: URL of the Oracle Object Storage Service that you noted down in a text file
      • User ID: OCID of a user that has access to the applicable bucket in Oracle Object Storage Service
      • Finger Print: The fingerprint that you saw and copied after you uploaded the public key in the Console. It looks something like this: 12:34:56:78:90:ab:cd:ef:12:34:56:78:90:ab:cd:ef
      • Tenant ID: Tenancy in the Oracle Infrastructure Cloud Console that you noted down in the text file
      • Private Key: Paste the private key contents that you previously downloaded
      • File Type: csv
      • CSV Delimiter: Delimiter for the data files
      • Date format for the data files must match the date format in your Oracle Object Storage Service source; for example, if you've used MM/dd/yyyy (01/23/1998) in your source, then you must specify the same format in CSV Date Format. See About Date and Timestamp Formatting for CSV File-based Extractors.
      • Timestamp format for the data files must match the timestamp format in your Oracle Object Storage Service source; for example, if you've used MM/dd/yyyy hh:mm:ss (01/23/1998 12:00:20) in your source, then you must specify the same format in CSV Timestamp Format

      
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for the Oracle Object Storage Service unless you perform a metadata extract.
   7. Click Save.
3. In Oracle Object Storage Service:
   1. Create the folder structure in the Bucket using these guidelines:
      

      • The base folder in the bucket must match with the details provided in the connection.
      • Inside the base folder, ensure to place each file in its own folder.
      • Ensure that the Prefix of Data_Store_Name (same as Folder name) and Files in the target folder match exactly.

      See the "To create a folder or subfolder" section in Using the Console.

   2. Inside the base folder, create the metadata file for the Data Store List. This file lists the supported data stores. Each data store is a folder that has the actual file used in data augmentation, for example, ASSETS. Ensure that the file name and folder name match and there aren’t any special characters (including space) in the datastore, folder or file names.
      
   3. Create the metadata file for each data file under the data store folder using these guidelines:

      The META_DATASTORES.csv must have these columns:

      • DATA_STORE_NAME - A mandatory column to identify the data store name.
      • DATA_STORE_LABEL - A non-mandatory column that identifies the description of the data store.

      Note:

      If you add new datastores to an existing source connection, make sure that META_DATASTORES.csv contains the full list of datastores, including the older existing datastores.

      Each folder must have:

      • A data file that has the actual data that gets loaded into NetSuite Analytics Warehouse. This file must have a prefix with the DATA STORE NAME.
      • A metadata file for the list of columns contains all the column information on the data. This file must have a Prefix with META_DATASTORES_<DATA_STORE_NAME>_COL.
        • For the columns in this metadata, ensure the following:
        • If column name is ABC, then metadata can be ABC or “ABC” - the double quotes are ignored.
        • If column name is “ABC”, then metadata must be “”ABC”” – the first double quotes are ignored.

      Example

      In the image, the folder name is ACTIVITY_TYPES. Hence, the data store name is ACTIVITY_TYPES. You can confirm this from the META_DATASTORES.csv file. In this example, the file is named ACTIVITY_TYPES.xlsx or ACTIVITY_TYPES.csv. The metadata file must be META_DATASTORES_ACTIVITY_TYPES_COL.csv.

      The META_DATASTORES_ACTIVITY_TYPES_COL.csv has these columns:

      • DATA_STORE_NAME - This is a mandatory column.
      • COLUMN_NAME - This is a mandatory column.
      • COLUMN_LABEL - This is a non-mandatory column.
      • DATA_TYPE – This is a mandatory column.
      • WIDTH – This column identifies the string length.
      • PRECISION - This column value must be Numeric data type.
      • SCALE - This column value must be Numeric data type.
      • KEY_SEQUENCE - This is a mandatory column that identifies the Primary Key definition. If you’re using the composite primary key, then use column order numbers as values.
4. In NetSuite Analytics Warehouse, on the Manage Connections page, select Actions for the Oracle Object Storage Service connection and then select Test Connection.
5. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
6. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the data from the Oracle Object Storage Service. Select the applicable source tables from the Oracle Object Storage Service data. See Augment Your Data.

As a service administrator, you can use the NetSuite Analytics Warehouse extract service to acquire data from a secure FTP source (SFTP) and use it to create data augmentations.

1. Check the port number for your secure FTP database and create a service request to get the port opened.

   Note:

   You must provide the IP address of the SFTP server, which should be a public IP and can’t be hostname and a fully qualified domain name (FQDN) or a class A private IP.
2. In NetSuite Analytics Warehouse, create the SFTP data connection using these instructions:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type, and then select SFTP as the connection type.
      
      

      

      
      
   5. In the dialog for the SFTP connection, select Standard in Connectivity Type, enter an email address to receive notifications in Notification Email, and provide applicable values in Remote Host, User Name, Private Key, Remote Host Extract Files Directory, File Type, CSV Delimiter, CSV Date Format, and CSV Timestamp Format. In Lock Metadata, specify whether you want to turn off the metadata extracts after first refresh if metadata isn't going to change. This option is useful if the flag to derive metadata from data files using the metadata utility is turned on in your source. In Remote Host, ensure that you specify an SFTP Server that supports FIPS Compliant key exchange algorithms.
      Ensure the following:

      • The table name and file name in your SFTP source needs to be the same.
      • The private key you provide is in the valid OpenSSH format and the minimum number of bits in the key should be 2048.
      • Date format for the data files must match the date format in your SFTP source; for example, if you've used MM/dd/yyyy (01/23/1998) in your source, then you must specify the same format in CSV Date Format. See About Date and Timestamp Formatting for CSV File-based Extractors.
      • Timestamp format for the data files must match the timestamp format in your SFTP source; for example, if you've used MM/dd/yyyy hh:mm:ss (01/23/1998 12:00:20) in your source, then you must specify the same format in CSV Timestamp Format.

      
      

      

      
      
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for SFTP unless you perform a metadata extract.
   7. Click Save.
3. In your SFTP source:
   1. Create the folder structure in the Bucket using these guidelines:
      

      • The base folder in the bucket must match with the details provided in the connection.
      • Inside the base folder, ensure to place each file in its own folder.
      • Ensure that the Prefix of Data_Store_Name (same as Folder name) and Files in the target folder match exactly.

      See the "To create a folder or subfolder" section in Using the Console.

   2. Inside the base folder, create the metadata file for the Data Store List. This file lists the supported data stores. Each data store is a folder that has the actual file used in data augmentation, for example, ASSETS. Ensure that the file name and folder name match and there aren’t any special characters (including space) in the datastore, folder or file names.
      
   3. Create the metadata file for each data file under the data store folder using these guidelines:

      The META_DATASTORES.csv must have these columns:

      • DATA_STORE_NAME - A mandatory column to identify the data store name.
      • DATA_STORE_LABEL - A non-mandatory column that identifies the description of the data store.

      Note:

      If you add new datastores to an existing source connection, make sure that META_DATASTORES.csv contains the full list of datastores, including the older existing datastores.

      Each folder must have:

      • A data file that has the actual data that gets loaded into NetSuite Analytics Warehouse. This file must have a prefix with the DATA STORE NAME.
      • A metadata file for the list of columns contains all the column information on the data. This file must have a Prefix with META_DATASTORES_<DATA_STORE_NAME>_COL.
        • For the columns in this metadata, ensure the following:
        • If column name is ABC, then metadata can be ABC or “ABC” - the double quotes are ignored.
        • If column name is “ABC”, then metadata must be “”ABC”” – the first double quotes are ignored.

      Example

      In the image, the folder name is ACTIVITY_TYPES. Hence, the data store name is ACTIVITY_TYPES. You can confirm this from the META_DATASTORES.csv file. In this example, the file is named ACTIVITY_TYPES.xlsx or ACTIVITY_TYPES.csv. The metadata file must be META_DATASTORES_ACTIVITY_TYPES_COL.csv.

      The META_DATASTORES_ACTIVITY_TYPES_COL.csv has these columns:

      • DATA_STORE_NAME - This is a mandatory column.
      • COLUMN_NAME - This is a mandatory column.
      • COLUMN_LABEL - This is a non-mandatory column.
      • DATA_TYPE – This is a mandatory column.
      • WIDTH – This column identifies the string length.
      • PRECISION - This column value must be Numeric data type.
      • SCALE - This column value must be Numeric data type.
      • KEY_SEQUENCE - This is a mandatory column that identifies the Primary Key definition. If you’re using the composite primary key, then use column order numbers as values.
4. On the Manage Connections page, select Actions for the SFTP connection and then select Test Connection.
5. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
6. After the connections are successfully established, navigate to the Data Augmentation tile on the Data Configuration page, select the connection in Data Source, and create a data augmentation using the SFTP data. Select the applicable secure FTP source tables. See Augment Your Data.

As a service administrator, you can use the Oracle NetSuite Analytics Warehouse extract service to acquire data from Azure Storage and use it to create data augmentations.

1. In Oracle NetSuite Analytics Warehouse, create the Azure Storage data connection using these instructions:
   1. In Oracle NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type, and then select Azure Storage as the connection type.
   5. In the dialog for the Azure Storage connection, select Standard in Connectivity Type, enter an email address to receive notifications in Notification Email, enter these Azure Storage instance details, and click Save:
      • Connectivity Type: Select Standard.
      • Notification Email: Enter an email address to receive notifications.
      • File Type: Select CSV.
      • Azure Blob Connection String: Enter your storage account's connection string.
      • Container: Specify the Azure container.
      • Remote Host Extract Files Directory: Name of the base folder in which you must place all your data files in Azure Storage.
      • CSV Delimiter: Delimiter for the data files.
      • CSV Date Format: Date format for the data files must match the date format in your Azure Storage source; for example, if you've used MM/dd/yyyy (01/23/1998) in your source, then you must specify the same format. See About Date and Timestamp Formatting for CSV File-based Extractors

      
      

      

      
      
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for Azure Storage unless you perform a metadata extract.
   7. Click Save.
2. In Oracle Object Storage Service:
   1. Create the folder structure in the Bucket using these guidelines:
      

      • The base folder in the bucket must match with the details provided in the connection.
      • Inside the base folder, ensure to place each file in its own folder.
      • Ensure that the Prefix of Data_Store_Name (same as Folder name) and Files in the target folder match exactly.

      See the "To create a folder or subfolder" section in Using the Console.

   2. Inside the base folder, create the metadata file for the Data Store List. This file lists the supported data stores. Each data store is a folder that has the actual file used in data augmentation, for example, ASSETS. Ensure that the file name and folder name match and there aren’t any special characters (including space) in the datastore, folder or file names.
      
   3. Create the metadata file for each data file under the data store folder using these guidelines:

      The META_DATASTORES.csv must have these columns:

      • DATA_STORE_NAME - A mandatory column to identify the data store name.
      • DATA_STORE_LABEL - A non-mandatory column that identifies the description of the data store.

      Note:

      If you add new datastores to an existing source connection, make sure that META_DATASTORES.csv contains the full list of datastores, including the older existing datastores.

      Each folder must have:

      • A data file that has the actual data that gets loaded into NetSuite Analytics Warehouse. This file must have a prefix with the DATA STORE NAME.
      • A metadata file for the list of columns contains all the column information on the data. This file must have a Prefix with META_DATASTORES_<DATA_STORE_NAME>_COL.
        • For the columns in this metadata, ensure the following:
        • If column name is ABC, then metadata can be ABC or “ABC” - the double quotes are ignored.
        • If column name is “ABC”, then metadata must be “”ABC”” – the first double quotes are ignored.

      Example

      In the image, the folder name is ACTIVITY_TYPES. Hence, the data store name is ACTIVITY_TYPES. You can confirm this from the META_DATASTORES.csv file. In this example, the file is named ACTIVITY_TYPES.xlsx or ACTIVITY_TYPES.csv. The metadata file must be META_DATASTORES_ACTIVITY_TYPES_COL.csv.

      The META_DATASTORES_ACTIVITY_TYPES_COL.csv has these columns:

      • DATA_STORE_NAME - This is a mandatory column.
      • COLUMN_NAME - This is a mandatory column.
      • COLUMN_LABEL - This is a non-mandatory column.
      • DATA_TYPE – This is a mandatory column.
      • WIDTH – This column identifies the string length.
      • PRECISION - This column value must be Numeric data type.
      • SCALE - This column value must be Numeric data type.
      • KEY_SEQUENCE - This is a mandatory column that identifies the Primary Key definition. If you’re using the composite primary key, then use column order numbers as values.
3. On the Manage Connections page, select Actions for the Azure Storage connection and then select Test Connection.
4. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
5. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the Azure Storage data. Select the applicable Azure Storage source tables. See Augment Your Data.

You can create additional connections to the NetSuite source based on your customer tier. These additional connections enable you to bring in data from multiple NetSuite accounts.

1. In NetSuite, complete these tasks:
   • Enable the Multi-Instance Connector (MIC) feature.
   • Add an integration record.
   • Create a token-based authentication permission and add it to a user.
   • Create a token.

   See Merging Your NetSuite Accounts Data Into Your NetSuite Analytics Warehouse Instance

2. In Oracle NetSuite Analytics Warehouse, Console, click Data Configuration under Application Administration.
3. On the Data Configuration page, click Manage Connections.
4. On the Manage Connections page, click Create and then click Connection.
5. In Create Connection, select Data Extraction in Usage Type and then select NetSuite 2 or an applicable NetSuite option such as NetSuite 3 or NetSuite 4 as the connection type.
6. In Create Connection, enter these details and then click Save:
   • Notification Email: Enter an email address to receive notifications.
   • User Name: Enter your user name.
   • JDBC URL: Enter the following URL: jdbc:ns://<NS Account ID>.connect.api.NetSuite.com:1708;ServerDataSource=NetSuiteDW;Encrypted=1;CustomProperties=(AccountID=<NS Account ID>;RoleID=57;Uppercase=1). Replace <NS Account ID> with your NetSuite Account ID.
   • Account ID: Enter your NetSuite Account ID.
   • Consumer Key: Paste the Consumer Key / Client ID string you noted down after creating the integration record.
   • Consumer Secret: Paste the Consumer Secret / Client Secret string you noted down after creating the integration record.
   • Token Key: Paste the Token ID string you noted down after creating the token.
   • Token Secret: Paste the Token Secret string you noted down after creating the token.
7. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
   You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

   Note:

   You can’t create augmentations for NetSuite 2 unless you perform a metadata extract.
8. On the Manage Connections page, select Actions for the NetSuite 2 connection and then select Test Connection.
9. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the NetSuite 2 data. Select the applicable NetSuite 2 source tables. See Augment Your Data.

As a service administrator, you can use the Oracle NetSuite Analytics Warehouse extract service to acquire data from Azure SQL and use it to create data augmentations.

1. In Oracle NetSuite Analytics Warehouse, create the Azure SQL data connection using these instructions:
   1. In Oracle NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select Azure SQL as the connection type.
      
      

      

      
      
   5. In the dialog for the Azure SQL connection, select Standard in Connectivity Type, enter an email address to receive notifications in Notification Email, enter your Azure SQL instance details, and click Save.
      
      

      

      
      
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for Azure SQL unless you perform a metadata extract.
2. On the Manage Connections page, select Actions for the Azure SQL connection and then select Test Connection.
3. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
4. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the Azure SQL data. Select the applicable Azure SQL source tables. See Augment Your Data.

As a service administrator, you can use the Oracle NetSuite Analytics Warehouse extract service to acquire data from Oracle Autonomous Database and use it to create data augmentations.

1. In Oracle NetSuite Analytics Warehouse, create the autonomous database connection using these instructions:
   1. In Oracle NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and depending on the number of connections, select options such as Oracle Autonomous Database 1, or Oracle Autonomous Database2 as the connection type.
      
      

      

      
      
   5. In the dialog for the Oracle Autonomous Database connection, provide these details in Source Connection and then click Save and Next:
      • Select Standard in Connectivity Type.
      • Enter an email address to receive notifications in Notification Email.
      • Enter the credentials to access the database in User Name and Password.
      • Enter the database service details in Service.
      • In Wallet, drag and drop the database wallet details.

      
      

      

      
      
   6. In the dialog for the Oracle Autonomous Database connection, provide these details in Extract Configuration and click Save:
      • In Incremental Strategy, select the incremental strategy (Flashback, Golden Gate, or RowSCN) that is compatible with your database configuration.
      • In Initial Extract Date Column Pattern, provide the initial extract date pattern that matches the pattern in your source.
      • In Last Update Date Column Pattern, provide the last update date pattern that matches the pattern in your source.
      • In Inclusion List, select Yes or No to include the mentioned list of datastores in the incremental strategy or not.

      • In List of Incremental datastores to include/exclude, enter a comma separated list of datastores names.

        If you don't provide, then the connector uses the incremental strategy for all the datastores. If you provide and IS_INCLUSION_LIST=true, only the provided list use the specified incremental strategy. If provided and IS_INCLUSION_LIST=false, the provided list won’t use the incremental strategy. If INCREMENTAL_STRATEGY property is available, then the connector uses IS_INCLUSION_LIST and INCR_DATASTORES_LIST for all strategies. If not available, then for FLASHBACK the connector checks if ENABLE_FLASHBACK_INCREMENTAL and FBA_INCR_DATASTORES_EXCEPTION_LIST is provided and for ROWSCN, it checks if ENABLE_ORA_ROWSCN_INCREMENTAL and ROWSCN_INCR_DATASTORES_EXCEPTION_LIST is provided.

      • In Case sensitive Data Stores, select Yes or No.
      • In Schema Name, enter the schema name to extract data from.
      • In Data Store Name Pattern, specify the name pattern of the data stores that you want extracted. If you provide this value, then the connector extracts only data stores matching the pattern.
      • Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection. You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

        Note:

        You can’t create augmentations for autonomous database unless you perform a metadata extract.

      
      

      

      
      
2. On the Manage Connections page, select Actions for the autonomous database connection and then select Test Connection.
3. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
4. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the autonomous database data. Select the applicable autonomous database source tables. See Augment Your Data.

As a service administrator, you can use the NetSuite Analytics Warehouse extract service to acquire data from the Enterprise Data Management Cloud instance and use it to create data augmentations.

1. In NetSuite Analytics Warehouse, create the Enterprise Data Management Cloud data connection using these instructions:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select Oracle Enterprise Data Management Cloud as the connection type.
      
      

      

      
      
   5. In the dialog for the Enterprise Data Management Cloud connection, select Standard in Connectivity Type, enter an email address to receive notifications in Notification Email, host name of the Oracle Enterprise Data Management server in Host Name, credentials to access the Enterprise Data Management Cloud instance in User Name and Password. In Extract Configuration, enter the list of extracts using only “comma” as the delimiter. The configuration extract must be a single line JSON without formatting for the quotation marks (" instead of \"), for example:

      [{"applicationName": "Account Reconciliation", "dimensionName": "Profiles","extractName": "Profiles"}]

      
      

      

      
      
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for Enterprise Data Management Cloud unless you perform a metadata extract.
   7. Click Save.
2. On the Manage Connections page, select Actions for the Enterprise Data Management Cloud connection and then select Test Connection.
3. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
4. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the Enterprise Data Management Cloud data. Select the applicable Enterprise Data Management Cloud source tables. See Augment Your Data.

As a service administrator, you can use the NetSuite Analytics Warehouse extract service to acquire data from the Enterprise Performance Management (EPM) SaaS instance and use it to create data augmentations for various Enterprise Resource Planning and Supply Chain Management use cases.

1. In EPM, create an integration, write out the results into a file whose name you provide in Download File Name, and then specify that same file name in List of Data Files while creating the connection to EPM in NetSuite Analytics Warehouse to extract the data.
   
2. In EPM, when exporting data, use one of the following modes:
   • Standard mode: This built-in workflow process helps manage the data flow process in and out of EPM. For Standard mode, you specify the period when you run the integration.
   • Quick mode: This process by-passes most of the steps in the workflow and provides a significant improvement in the extract process. When using quick mode, you specify the period in the integration definition filter, and this may include a substitution variable.When using the API, the same rules apply, except that when you need to specify a period, you can’t use a substitution variable to pull from EssBase, but will need to include the period or year on extract.

   See Exporting Data.

3. In NetSuite Analytics Warehouse, create the EPM data connection using these instructions:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select the connection type based on the functional module that you want to connect to. For example, to connect to the "Financial Close and Consolidation (FCCS)" module, select Oracle EPM - Financial Close and Consolidation as the connection type.
      
      

      

      
      
   5. In the dialog for the Oracle EPM connection, provide these details in Source Connection and then click Save and Next:

      Note:

      Some properties are defined on the Extract Configuration tab. These properties are visible, but read-only on the Source Connection tab.

      • Connectivity Type: Select Standard.
      • Notification Email: Enter an email address to receive notifications.
      • User Name and Password: Enter the credentials for your EPM source. Prefix the user name with the domain of your EPM source, such as domain.username.
      • URL: Enter the specific URL of your EPM source using the https://<DOMAIN_NAME>region.ocs.oraclecloud.com format. For example, https://epm7-test-a123456.epm.us6.oraclecloud.com. Ensure to use the https:// protocol to avoid a timeout error.
   6. In the Extract Configuration page of the Oracle EPM connection dialog, provide these details and click Save:
      • Select Integration Job from the Job Mode dropdown, then provide the Job Name and Period Name details in the Extract Configuration section.
        • Job Name: Provide the EPM job name such as "Job 1" and period name such as "Quarter 1" corresponding to the given job name to extract data for specific periods. This enables you to run multiple EPM jobs. To ensure a healthy connection, you should first run the desired EPM integration jobs in the EPM user interface. Then enter the successfully-run and valid EPM integration job names in this field. If you’ve multiple job names, ensure that they are comma separated. For example, FAWEPMTestingV2, FAWEXPORT,FAW Job Testing.
        • Period Name: Provide the period names for the corresponding job names. Ensure that multiple period names are comma separated. You may leave this blank, in which case the connector uses the global point of view. Few examples of period names are:
          • {Jan-22},{Oct-22}
          • {Nov-22}{Dec-22},{Jan-23}{Feb-23}
          • {Jan#FY20}{Mar#FY20},{Apr#FY20}{May#FY20}
      • Alternatively, select Data Files from the Job Mode dropdown, then provide the
        • List of Data Files: Specify the file name that you had entered in Download File Name while creating an integration in EPM.

      
      

      

      
      
   7. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for EPM unless you perform a metadata extract.
   8. Click Save.
4. On the Manage Connections page, select Actions for the EPM connection and then select Test Connection.
5. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
6. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the EPM data. Select the applicable EPM source tables. See Augment Your Data.

As a service administrator, you can use the NetSuite Analytics Warehouse extract service to acquire data from EPM Export Data instance and use it to create data augmentations.

1. From your EPM instance, collect the application name and job names to provide in the source connection configuration tab on the connection dialog in NetSuite Analytics Warehouse using these instructions:
   1. On the main dashboard page of the EPM instance, select Application and then Overview.
   2. On the Overview page, note down the Application Name to use in the connection properties.
   3. To fetch the job names, on the right side of the Overview page, select the Actions drop down and then select Export Data.
   4. In the Export Data dialog, the job names appear as a list. Each job name is a separate job with a unique job name. You may see one or no job name(s).
2. In EPM, when exporting data, use one of the following modes:
   • Standard mode: This built-in workflow process helps manage the data flow process in and out of EPM. For Standard mode, you specify the period when you run the integration.
   • Quick mode: This process by-passes most of the steps in the workflow and provides a significant improvement in the extract process. When using quick mode, you specify the period in the integration definition filter, and this may include a substitution variable. When using the API, the same rules apply, except that when you need to specify a period, you can’t use a substitution variable to pull from EssBase, but will need to include the period or year on extract.

   See Exporting Data.

3. In NetSuite Analytics Warehouse, create the data connection to the EPM Export Data instance using these instructions:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select the connection type based on the functional module that you want to connect to. For example, to connect to the "Financial Close and Consolidation (FCCS)" module, select EPM Financial Close and Consolidation Data Export as the connection type.
      
      

      

      
      
   5. In Create Connection for the EPM source, enter these details and click Save:
      • Connectivity Type: Select Standard.
      • Notification Email: Enter an email address to receive notifications.
      • URL: Enter the specific URL of your EPM source ensuring to use https. For example, https://epm7-test-a123456.epm.us6.oraclecloud.com. If you provide http://epm7-test-a123456.epm.us6.oraclecloud.com or epm7-test-a123456.epm.us6.oraclecloud.com, then the system rejects it.
      • User Name and Password: Enter the credentials for your EPM source. Prefix the user name with the domain of your EPM source, such as domain.username.
      • EPM Application Name: Specify the file name that you had entered in Download File Name while creating the EPM data exchange job in EPM.
      • Comma Separated Job Names: Provide the EPM data exchange job names. If you’ve multiple job names, then ensure that they are comma separated. For example, FAWEPMTestingV2, FAWEXPORT,FAW Job Testing.
      • Extract Configuration: Provide the EPM data exchange job name in Job Name and select Dimension Export if you want to extract the hierarchy (dimensional data) from your EPM Export Data instance.

      
      

      

      
      
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for EPM unless you perform a metadata extract.
   7. Click Save.
4. On the Manage Connections page, select Actions for the EPM connection and then select Test Connection.
5. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
6. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the EPM data. Select the applicable EPM source tables. See Augment Your Data.

As a service administrator, you can use the Oracle NetSuite Analytics Warehouse extract service to acquire data from a Fusion Supply Chain Planning instance.

1. In NetSuite Analytics Warehouse, create the Fusion Supply Chain Planning data connection:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select Fusion Supply Chain Planning as the connection type.
      
      

      

      
      
   5. In the Create Connection dialog, enter these details and then click Save:
      • Connectivity Type: Standard.
      • Notification Email: An email address to receive notifications.
      • SCP Plan Names: The applicable plan names in your Fusion Supply Chain Planning instance.

      Note:

      If you have multiple Supply Chain Plan names, enter them in a comma-separated list with no spaces between plan names.

      
      

      

      
      
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for Fusion Supply Chain Planning unless you perform a metadata extract.
2. On the Manage Connections page, select Actions for the Fusion Supply Chain Planning connection and then select Test Connection.
3. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
4. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the Fusion Supply Chain Planning data. Select the applicable Fusion Supply Chain Planning source tables. See Augment Your Data.

As a service administrator, you can use the NetSuite Analytics Warehouse extract service to acquire data from the Google Analytics SaaS instance and use it to create data augmentations for various Enterprise Resource Planning and Supply Chain Management use cases.

1. In Google Cloud (Analytics) Project, create a service account and download the credentials.json file.
   See https://cloud.google.com/docs/authentication/production.
2. Add the service account to the Google Analytics 4 property.
   See https://developers.google.com/analytics/devguides/reporting/data/v1/quickstart-client-libraries.
3. Enable Google Analytics APIs using these instructions:
   1. Using a text editor, open the credentials.json file that you had downloaded and search for the client_email field to obtain the service account email address.
   2. Use this email address to add a user to the Google Analytics 4 property you want to access through the Google Analytics Data API v1.
   
4. Ensure that the Google Analytics Admin API, Google Analytics Data API are available for your Google Analytics instance.
   
5. In NetSuite Analytics Warehouse, create the Google Analytics data connection using these instructions:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select Google Analytics as the connection type.
      
      

      

      
      
   5. In the dialog for the Google Analytics connection, select Standard as the connectivity type and enter these details:
      • Notification Email: An email address to receive notifications regarding this connection.
      • Service Account Credentials Json File: The Google Cloud Service Account credentials.json file that you had downloaded.
      • Account ID: Google Analytics account ID.
      • GA4 List of Property ID: The GA4 List of Property ID with commas to separate each ID.
      • Lookback Mode: Select either Full or Committed.
      • List of Lookback N days Ago: Comma separated list of days (integer) values such as 7,21.

      

      Note these:

      • For the Lookback mode, if you don't provide a value, then the Lookback mode isn't supported. The Full option requires one day value, if you provide multiple values, then the process uses the first value. You can provide multiple values for the Committed option.
      • For List Data Stores, the REST API returns a list of GA4 Property IDs either using the Account ID (if provided) or just the source configured or provided list of property.
      • For List columns, the REST API returns a list of column metadata for the given GA4 Property ID.
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for Google Analytics unless you perform a metadata extract.

      Metadata extract:

      • Retrieves metadata columns for each GA4 Property ID provided in the source configuration.
      • Prefixes the GA property columns with Dimension_ orMetric_ that NetSuite Analytics Warehouse later uses while extracting data to differentiate Dimension and Metric column type.
      • Leaves the payload dataStores array empty.
   7. Click Save.
6. On the Manage Connections page, select Actions for the Google Analytics connection and then select Test Connection.

   Note:

   REST API signature is same across sources. Test connection invokes GA Common Metadata API. This returns the default version values and no calls are made to the source.
7. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
8. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the Google Analytics data. Select the applicable Google Analytics source tables. Ensure that you specify “Dimension_transactionId” in the source tables as the primary key and use it to join each of the data augmentation tables. You can select a maximum number of nine dimensions for each data augmentation. See Augment Your Data.
   When you enable data extraction, you can schedule to run when you choose to do so. For data extraction, note these:

   1. Provide date ranges to run the report and fetch data.
   2. Regular data extract uses the initial or last ExtractDate as StartDate and job RunDate as EndDate.
   3. Lookback mode includes additional date ranges along with the regular extract date range which fetches additional data set but in a single runReport call.
      • The Full option has a single date range; StartDate=ExtractDate - NdaysAgo, EndDate=RunDate.
      • The Commited option can have multiple date ranges. For each configured GA_LIST_OF_N_DAYS_AGO, StartDate=ExtractDate - NdaysAgo, EndDate=RunDate - NdaysAgo.

As a service administrator, you can use the NetSuite Analytics Warehouse extract service to acquire data from the Mongo database and use it to create data augmentations.

1. Check the port number for your Mongo database and create a service request with server host and port details to enable network connectivity to the Mongo database server.
2. In NetSuite Analytics Warehouse, create the Mongo database connection using these instructions:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select MongoDB as the connection type.
      
      

      

      
      
   5. In the dialog for the Mongo database connection, select Standard in Connectivity Type, enter an email address to receive notifications in Notification Email, and provide these details:
      • Connection Protocol: Enter the connection protocol such as mongodb+srv or mongodb
      • Credentials to access the database in User Name and Password
      • Host Name: Enter the host name of the Mongo database such as cluster0.example4.mongodb.net
      • Host Port: Enter the port number where Mobgo database is listening such as 27017
      • Database Name: Enter a name such as Analytics
      • Last Update Date Column Pattern: Enter pattern such as "%mo%fie%te%"

        Note:

        If column name matches with pattern provided, then NetSuite Analytics Warehouse marks the column name with isLastUpdateDate = true and uses it for the incremental extract. For example, if pattern provided is “%mo%fie%te%“, then the column name modifiedDate is marked as isLastUpdateDate = true.
      • Initial Extract Date Column Pattern: Enter pattern such as "%cr%ted%te%"

        Note:

        If column name matches with pattern provided, then NetSuite Analytics Warehouse marks the column name with isCreationDate = true and uses it for the initial extract date extraction. For example, if pattern provided is: “%cr%ted%te%“, then the column name createdDate is marked as isCreationDate = true.

      
      

      

      
      
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for Mongo database unless you perform a metadata extract.
   7. Click Save.
3. On the Manage Connections page, select Actions for the Mongo database connection and then select Test Connection.
4. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
5. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the Mongo database data. Select the applicable Mongo database source tables. See Augment Your Data.

As a service administrator, you can use the NetSuite Analytics Warehouse extract service to acquire data from the MySQL Cloud database and use it to create data augmentations.

1. Check the port number for your MySQL Cloud database and create a service request with server host and port details to enable network connectivity to the MySQL server.
2. In NetSuite Analytics Warehouse, create the MySQL Cloud database connection using these instructions:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select MySQL Cloud as the connection type.
      
      

      

      
      
   5. In the dialog for the MySQL Cloud database connection, select Standard in Connectivity Type, enter an email address to receive notifications in Notification Email, and provide these details:
      • Host Name: Enter the host name of MySQL server such as 100.111.252.64
      • Port Number: Enter the port number where the server is listening such as 3306
      • Database: Enter the database name you need to connect to such as airportdb
      • Credentials to access the database in User Name and Password
      • Last Update Date Column Pattern: Enter format such as "%mo%fie%te%"

        Note:

        If column name matches with pattern provided, then NetSuite Analytics Warehouse marks the column name with isLastUpdateDate = true and uses it for the incremental extract. For example, if pattern provided is “%mo%fie%te%“, then the column name modifiedDate is marked as isLastUpdateDate = true.
      • Initial Extract Date Column Pattern: Enter format such as "%cr%ted%te%"

        Note:

        If column name matches with pattern provided, then NetSuite Analytics Warehouse marks the column name with isCreationDate = true and uses it for the initial extract date extraction. For example, if pattern provided is: “%cr%ted%te%“, then the column name createdDate is marked as isCreationDate = true.

      
      

      

      
      
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for MySQL Cloud database unless you perform a metadata extract.
   7. Click Save.
3. On the Manage Connections page, select Actions for the MySQL Cloud database connection and then select Test Connection.
4. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
5. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the MySQL Cloud database data. Select the applicable MySQL Cloud database source tables. See Augment Your Data.

As a service administrator, you can use the NetSuite Analytics Warehouse extract service to acquire data from the Analytics Publisher reports and use it to create data augmentations for various use cases.

1. In Analytics Publisher, build a comma separated values (CSV) report for the targeted tables. See Create Reports and Generate CSV Output.
2. In NetSuite Analytics Warehouse, create the data connection to the Analytics Publisher reports using these instructions:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select BI Publisher as the connection type.
      
      

      

      
      
   5. In the dialog for the BI Publisher connection, provide these details in Source Connection and then click Save and Next:
      • Enter an email address to receive notifications in Notification Email.
      • Host name of the BI Publisher in Host Name.
      • Credentials to access your BI Publisher in User Name and Password

      
      

      

      
      
   6. In the dialog for the BI Publisher connection, provide these details in Extract Configuration and then click Save:
      • In Reports Configuration, provide the path of the report in Oracle Analytics Publisher, select Run Asynchronously if the report takes a long time to execute, else select Run Synchronously In Chunk if the report has a large volume of data (more than 7 MB in size) because this enables you to download the report in chunks of data.

        Note:

        • If the report execution is completed before the timeout limit and generates large volume of data then set runInChunkMode: true. The recommendation is to set runInChunkMode to true if the report output file is over 7MB.
        • If the report has parameters defined, then provide the values in the "params" section of the report configuration array. If the value in the params array is set with placeholder __lastUpdateDate__, then the connector applies the initial extract date or last successful extract date to the param.
      • For CSV Date Format and CSV Timestamp Format, see About Date and Timestamp Formatting for CSV File-based Extractors.

        Note:

        Ensure that the date format used in Oracle Analytics Publisher and NetSuite Analytics Warehouse match.

      
      

      

      
      
   7. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for the BI Publisher reports unless you perform a metadata extract.
   8. Click Save.
3. On the Manage Connections page, select Actions for the BI Publisher reports connection and then select Test Connection.
4. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the data augmentations and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
5. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the BI Publisher reports data. Select the applicable BI Publisher source tables. See Augment Your Data.

As a service administrator, you can use an extract service remote agent to connect to an Oracle database using JDBC and use the data to create data augmentations.

1. Set up the remote agent to load data from your SQL Server into Oracle NetSuite Analytics Warehouse.
   See Set up the Remote Agent to Load Data into NetSuite Analytics Warehouse (Preview).
2. Configure the remote agent and Oracle database data source on the Data Configuration page in Oracle NetSuite Analytics Warehouse using these instructions:
   1. On the Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type, and then select Oracle JDBC as the connection type.
      
      
      
      
   5. In Create Connection for Oracle JDBC, in Connectivity Type, verify that Remote is selected automatically.
      
      
      
      
   6. In Remote Agent, select the remote agent connection that you created earlier, for example, Remote Agent.
   7. Enter an email address to receive notifications in Notification Email, provide credentials for the Oracle database source in User Name and Password, and the URL of the Oracle database source in URL.
   8. In Initial Extract Date Column Pattern, provide the initial extract date pattern that matches the pattern in your source.
   9. In Last Update Date Column Pattern, provide the last update date pattern that matches the pattern in your source.
   10. If your source has flashback support, then select Yes in Enable flashback incremental.
   11. In List of Flashback Incremental Exempt datastores, provide a comma separated list of datastores that you want to exempt from the flashback incremental queries.
   12. If your source has ROWSCN support, then select Yes in Enable ROWSCN incremental.
   13. In List of ROWSCN Incremental Exempt datastores, specify a comma-separated list of datastores that you want to exclude from the automatic tracking of row changes based on system change numbers.
   14. In Case Sensitive Data Stores, select Yes or No to specify whether the datastores have case sensitive data.
   15. In Schema Name, enter the schema name to extract data from.
   16. In Data Store Name Pattern, specify the name pattern of the datastores that you want extracted. If you provide this value, then the connector extracts only datastores matching the pattern.
   17. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection. You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

       Note:

       You can’t create augmentations for Oracle database unless you perform a metadata extract.
   18. Click Save.
3. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
4. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the Oracle database data. Select the applicable Oracle database source tables. See Augment Your Data.

As a service administrator, you can use the NetSuite Analytics Warehouse extract service to acquire data from the Salesforce SaaS instance and use it to create data augmentations.

1. Run the following command in your terminal to generate a keystore file:

   openssl pkcs12 -export -inkey server.key -in server.crt -name YOUR_KEY_NAME -passout pass:YOUR_PASSWORD -out keystorefile.p12

   Replace YOUR_KEY_NAME and YOUR_PASSWORD with your desired values, and save the generated keystorefile.p12 file to later upload it while creating the connection to your Salesforce source.

2. In NetSuite Analytics Warehouse, create the Salesforce data connection using these instructions:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select Salesforce as the connection type.
      
      

      

      
      
   5. In the dialog box for the Salesforce connection, select Standard in Connectivity Type and enter an email address to receive notifications in Notification Email.
      
      

      

      
      
   6. In Authorization Type, you can select Basic Authentication or OAuth as the type of authorization.
      If you select Basic Authentication, then:

      • Enter the credentials for your Salesforce source in User Name and Password. The user stated here must have access to all the data in the Salesforce system to extract it to the warehouse.
      • Copy and paste the security token from your Salesforce account in Security Token. This is an alpha-numeric code and may contain special characters, however, it isn't visible. It's encrypted and shown as ….
      • In Is Sandbox Environment, select Yes if your Salesforce source is a test or sandbox environment; else select No.

      If you select OAuth, then enter these additional values:

      • In Client ID, enter the unique identifier that represents the Salesforce application within the Salesforce instance and is visible when you sign into your Salesforce account.
      • In KeyStore File, provide the file that is in a PKCS#12 file format (denoted by .p12 extension), which can contain both private keys and certificates (such as the public key of the server or the signing certificate).
      • In KeyStore Password, enter the password for accessing the specified keystore file.
      • In Keystore Key name, provide the unique name (identifier or alias) of the specified keystore file.
   7. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for Salesforce unless you perform a metadata extract.
   8. Click Save.
3. On the Manage Connections page, select Actions for the Salesforce connection and then select Test Connection.
4. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
5. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the Salesforce data. Select the applicable Salesforce source tables. See Augment Your Data.

As a service administrator, you can use the NetSuite Analytics Warehouse extract service to acquire data from the Shopify SaaS instance and use it to create data augmentations for various Enterprise Resource Planning and Supply Chain Management use cases.

1. In NetSuite Analytics Warehouse, create the Shopify data connection using these instructions:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select Shopify as the connection type.
      
      

      

      
      
   5. In the dialog for the Shopify connection, enter an email address to receive notifications in Notification Email, applicable token value in Access Token, Store Name such as myfawteststore.myshopify.com, and True in Bulk Extract.
      
      

      

      
      
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for Shopify unless you perform a metadata extract.
   7. Click Save.
2. On the Manage Connections page, select Actions for the Shopify connection and then select Test Connection.
3. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
4. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the Shopify data. Select the applicable Shopify source tables. See Augment Your Data.

If you’ve subscribed for Oracle Fusion CX Analytics and want to load data from your Oracle Eloqua source into NetSuite Analytics Warehouse, then create a connection using the Eloqua connection type.

1. In Oracle NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
2. On the Data Configuration page, click Manage Connections under Global Configurations.
3. On the Manage Connections page, click Create and then click Connection.
4. In Create Connection, select Data Extraction in Usage Type, and then select Oracle Eloqua as the connection type.
   
   

   

   
   
5. In the dialog for the Eloqua connection, select Standard in Connectivity Type, enter an email address to receive notifications in Notification Email, and the credentials to connect with the Eloqua source in User Name and Password.
6. In URL, enter the URL for your Eloqua server in this sample format: https://<your eloqua server>/api/odata.
   Description of the illustration fawag-eloqua-create-connection.png
7. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
   You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

   Note:

   You can’t create augmentations for Eloqua unless you perform a metadata extract.
8. Click Save.
9. On the Manage Connections page, select Actions for the Eloqua connection and then select Test Connection.
10. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

    Note:

    Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
11. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the Eloqua data. Select the applicable Eloqua source tables. See Augment Your Data.

As a service administrator, you can use the Oracle NetSuite Analytics Warehouse extract service to acquire data from QuickBooks Online and use it to create data augmentations.

1. In Oracle NetSuite Analytics Warehouse, create the QuickBooks Online data connection using these instructions:
   1. In Oracle NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select QuickBooks Online as the connection type.
      
      

      

      
      
   5. In the dialog for the QuickBooks Online connection, enter these details and click Save:
      • Connectivity Type: Select Standard.
      • Notification Email: Enter an email address to receive notifications.
      • URL: Enter the complete URL of your Quickbooks Online instance.
      • Client ID: This is the unique ID on your Quickbooks Online instance.
      • Client Secret: Copy and paste the entire "client secret" from your authentication.
      • Refresh Token: This token is changed by Quickbooks everyday; enter your refresh token for the day you want to execute this pipeline.
      • Company ID: This is your company ID for the Quickbooks Online instance.

      
      

      

      
      
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for QuickBooks Online unless you perform a metadata extract.
   7. Click Save.
2. On the Manage Connections page, select Actions for the QuickBooks Online connection and then select Test Connection.
3. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
4. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the QuickBooks Online data. Select the applicable QuickBooks Online source tables. See Augment Your Data.

As a service administrator, you can use the Oracle NetSuite Analytics Warehouse extract service to acquire data from a Snowflake instance.

1. In NetSuite Analytics Warehouse, create the Snowflake data connection:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select Snowflake as the connection type.
      
   5. In Create Connection, enter these details and then click Save:
      • Notification Email: An email address to receive notifications.
      • Auth Schema: Select the applicable option:
        • Basic Authentication to establish the connection and provide the username and password.
          
          
          
          
        • Private Key if you’re using token-based authentication.
          
          
          
          
      • Host Name: Complete host name of your Snowflake instance.
      • Table Schema: Your Snowflake table schema such as TPCH_SF1.
      • Database: Mentioned in your Snowflake account under Data.
      • Warehouse: The compute resources in your Snowflake instance that you can find by running SHOW WAREHOUSES [ LIKE '<pattern>' ]. See SHOW WAREHOUSES.
      • Private Key: Generate the Private Key in Snowflake, if you don’t have one already, and paste it here. See Generate the Private Key.
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for Snowflake unless you perform a metadata extract.
   7. Click Save.
2. On the Manage Connections page, select Actions for the Snowflake connection and then select Test Connection.
3. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the data augmentations and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
4. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the Snowflake data. Select the applicable Snowflake source tables. See Augment Your Data.

As a service administrator, you can use the NetSuite Analytics Warehouse extract service to acquire data from the Taleo instance and use it to create data augmentations for various Enterprise Resource Planning and Supply Chain Management use cases.

1. In NetSuite Analytics Warehouse, create the Taleo data connection using these instructions:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select Taleo as the connection type.
      
      

      

      
      
   5. In Connectivity Type, select Standard, enter an email address to receive notifications in Notification Email, host name of your Taleo instance in Host Name, and credentials for your Taleo source in User Name and Password.
      
      

      

      
      
   6. Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection.
      You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

      Note:

      You can’t create augmentations for Taleo unless you perform a metadata extract.
   7. Click Save.
2. On the Manage Connections page, select Actions for the Taleo connection and then select Test Connection.
3. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
4. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the Taleo data. Select the applicable Taleo source tables. See Augment Your Data. See Augment Your Data.

As a service administrator, you can use the Oracle NetSuite Analytics Warehouse extract service to acquire data from an Oracle Transportation Management Cloud Service SaaS instance.

1. Create a confidential application in Oracle Identity Cloud Service. See IDCS Configuration.
2. In Oracle Transportation Management Cloud Service, create a user and assign the "Integration" role using these instructions:
   1. Navigate to the User Manager with DBA.Admin privileges and click New in User Finder.
   2. In User Name, enter a name of your choice.
   3. Enter the client ID from the Oracle Identity Cloud Service confidential application in Nickname.
   4. In Domain Name, enter the name of the target domain.
   5. Enter a compliant Password and the password confirmation.
   6. In User Role ID, select INTEGRATION.
   7. In Access Control List add the list named Data Replication Service - Rest with the Granted option selected.
   8. Select Finished.
3. Obtain the OAuth 2 client credentials from your Oracle Transportation Management Cloud Service instance to provide while creating the data connection. See OAuth 2.
4. In NetSuite Analytics Warehouse, enable Oracle Transportation Management on the Enable Features page. See Make Preview Features Available.
5. In NetSuite Analytics Warehouse, create the Oracle Transportation Management Cloud Service data connection:
   1. In NetSuite Analytics Warehouse Console, click Data Configuration under Application Administration.
   2. On the Data Configuration page, click Manage Connections.
   3. On the Manage Connections page, click Create and then click Connection.
   4. In Create Connection, select Data Extraction in Usage Type and then select Oracle Transportation Management as the connection type.
      
      

      

      
      
   5. In the dialog for the Oracle Transportation Management Cloud Service connection, enter these details and then click Save:
      • Connectivity Type: Standard.
      • Notification Email: An email address to receive notifications.
      • Scope: The scope name defined in your Oracle Transportation Management Cloud Service's Oracle Identity Cloud Service application’s configuration.
      • Token URL: The URL from which to obtain the access token. See Runtime Configuration.
      • URL: Source application URL (hostname and protocol of the Oracle Transportation Management Cloud Service instance).
      • Client ID: The unique client identifier generated during OAuth registration process.
      • Client Secret: The client secret generated during the OAuth registration process (a private key similar to a password that is generated when registering your Oracle Transportation Management Cloud Service's Oracle Identity Cloud Service application).
      • Verify that the Refresh Metadata toggle is enabled to ensure that the metadata is refreshed when you save the connection. You can later refresh the metadata from the Actions menu on the Manage Connections page, if required.

        Note:

        You can’t create augmentations for Oracle Transportation Management Cloud Service unless you perform a metadata extract.

      
      

      

      
      
6. On the Manage Connections page, select Actions for the Oracle Transportation Management Cloud Service connection and then select Test Connection.
7. To delete a connection, on the Manage Connections page, click Actions for the applicable connection and select Delete.

   Note:

   Ensure that you delete the functional areas, data augmentations, and custom data configurations related to the data connection before deleting it. You can't update or load data from deleted data connections to the warehouse.
8. After the connections are successfully established, navigate to the Data Configuration page, select the connection in Data Source, then click the Data Augmentation tile, and create a data augmentation using the Oracle Transportation Management Cloud Service data. Select the applicable Oracle Transportation Management Cloud Service source tables. See Augment Your Data.