Creating CSV Dataset

Rubiscape supports CSV files (.csv) for dataset creation.

To create a CSV dataset, follow the steps given below.

1. On the home page, click Create icon ().

   The Product Selection page is displayed.

2. Hover over the Data Connect tile and click Create Dataset.
   
   
   
   

   Dataset Selection page is displayed.

3. From the File options, select CSV.
   
   
   
   

   Create CSV Dataset page is displayed.

You can use a CSV file stored on your computer or from the AWS S3 storage network to create a CSV dataset.

The dataset creation for these types is explained in the sections below.

Creating CSV Dataset by Uploading CSV File

To create a CSV dataset by uploading a CSV file from your computer, follow the steps given below.

1. Follow steps 1 to 3 of Creating CSV Dataset.

2. Enter Name and Description for your dataset.

3. Click Select Encoding dropdown to select the desired encoding method. The default encoding method is utf_8.

4. Click Source of Data dropdown to select the desired source options. Available options are S3, FTP, Azure Blob Storage, File Path.

5. Click Browse. The File Browser window is displayed.

6. Browse to your file location and select a CSV file. The Features (columns) in the CSV worksheet are displayed in the Features box.

7. To change the datatype of the features, refer to Configuring Feature Type.

8. If you wish to remove any of the features, hover over the feature name, and click the Close icon ().
   
   
   
   

9. Click Create.
   
   
    

A confirmation message is displayed.
The CSV dataset is created in the current workspace and available in workbooks, workflows, and dashboards.

Creating CSV Dataset using S3 Bucket Storage

To create a CSV dataset by uploading a CSV file from the AWS S3 bucket storage, follow the steps given below.

1. Follow steps 1 to 3 of Creating CSV Dataset.

2. Enter Name and Description for your dataset.

3. To upload a file from AWS storage, select the S3 option from Source of Data dropdown.
   A new set of dataset creation options are displayed.

4. Enter the following details for the cloud storage.

   • Bucket Name

   • Aws Access Key Id

   • Aws Secret Access Key

   • File Directory URL (for the folder created by you on the S3 browser)

   • Filename or Wildcard
     
     

     Notes:

     By default, the number in the Skip Rows field is zero (0). You can enter an integer value in the field corresponding to the number of rows in the dataset you wish to delete. For example, if you enter 15 in the Skip Rows field, the first 15 rows will be skipped/deleted while creating the dataset. The administrator provides Aws Access Key Id and Aws Secret Access Key. If a file is already present in the root directory, you can access the file from the folder using a slash (/) symbol. If a folder is already present, you can give its path in the File Directory URL field. The File Directory URL is for the folder created by you on the S3 browser. This folder contains the dataset files whose Filename/Wildcard is mentioned in the next field. You can use special characters/symbols to search file names like an asterisk (*) and question mark (?). An asterisk (*) symbol searches file names with multiple (any number) characters in the specified place. For example, a filename with Data_*_* searches all file names containing multiple characters between the underscore marks and after the last underscore mark. A question mark (?) is used to search file names with a single character in the specified place. For example, a filename with Data_? _??? searches all file names containing one character between the underscore marks and three characters after the last underscore mark. Hence, a filename with Data_??_* searches all file names containing two characters between the underscore marks and multiple characters after the last underscore mark. You can search for the dataset files in all folders and sub-folders of the root directory by selecting the Traverse Subdirectory checkbox.

5. To validate the connection parameters, click Verify.

   If the parameters are valid, a Verification Success message is displayed. Also, the Show Filename(s) button gets activated.

6. To see the files detected, click Show Filename(s).

   The list of detected files is displayed in a separate window.
   
   

   Also, the features (columns) in the CSV worksheet are displayed in the Features box.

7. To change the datatype of the features, refer to Configuring Feature Type.

8. If you wish to remove any of the features, hover over the feature name, and click the Close icon ().

9. To insert additional features (along with the features already present in the dataset), click Additional Output Features.
   Create CSV Dataset window is displayed. It displays the following features, which can be inserted along with the existing ones.
   
   

   • Full File Name

   • Short File Name

   • File Path

   • File Extension
     
     

   Notes:

   The Additional Output Features are not selected by default. You can choose anyone, multiple, or all of them to be inserted into the dataset.

10. Select the checkboxes corresponding to those features that you want to insert and click Done.

11. Click Create.
    
    

    Notes:

    Make sure to use a standard date format for datasets containing date as a variable. In case the dataset contains geographical variables, the Map Locations page is displayed. Verify that all locations are mapped and click Done to create the dataset. Refer to Creating a Dataset with Geographical Variable Type.

The CSV dataset is created in the current workspace and is available for use in workbooks, workflows, and dashboards.

Notes

Enabling the "Disable Cache" option allows you to create a dataset without generating a dataset cache. When you select to "Disable Cache", the dashboard will not offer the "Enable Direct Query" option. For more information, please refer to the "Enable Direct Query" document.