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Applies to: (tick) Kyvos Enterprise  (tick) Kyvos Cloud (SaaS on AWS) (tick) Kyvos AWS Marketplace

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Note

Kyvos does not support some special characters/values. Either remove them from the source data or contact the Kyvos Support team for assistance.
Example of unsupported values: [New line character 'n', HTML tags, KYVOS_NULL_VALUE, !==!, ~, !__!, !@!, !@@!]

Formatting data

Kyvos allows you to change the data type for columns while registering datasets. The supported Data Types are char, date, and number.

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  • If the raw data contains decimal values, Kyvos detects it as a  Double, by default. If you do not want any precision loss post cube build-in querying, then set the  Data Type  of the respective field as  Decimal  and set the correct  precisions  and  scale  to avoid any data inaccuracy and precision loss. Cube build size may increase if the data type is  Decimal.

  • From the Kyvos 2023.1 release onwards, the Decimal data type is applicable only when registering a dataset using a BigQuery connection. The Decimal data type will only be applicable to new Register datasets, while the previous data types (long, float, and double) will be used for existing register files.

Working with Dates

Generally, date columns store dates as strings. Kyvos then reads the date using the default format YYYY/MM/DD.  If the date format of the source data doesn't match the default format, then Kyvos will throw an error while validating the registered dataset. To improve performance and avoid errors, you should specify the format string for the date.

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  1. From the Toolbox, select the object type, such as  Files.

  2. Select a registered dataset.

  3. Click the icon to view the Column list if needed.

  4. Click  Filter data.

  5. Enter the field, criteria, compare value, value, and to create complex filters, use parentheses, And or Or, and click the  Plus icon  to add more additional criteria.

  6. Click  Apply. See Using filters to learn more
            For  Ex: You want to filter the data as per the nation "Canada". 
                         a. Click on  column c_nation.
                         b. Click on  Filter data.
                         c.  Fill in the details.
                         d.  Click on  Apply.
                         e.  Click on  Preview table 
    Now the table is shown below.

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