This section provides information on improvements to
Mismatched values are no longer published as null values in CSV outputs
In prior releases, when a file was published in CSV format, any values that were mismatches for a column's data type were written as null values, which could lead to loss of data that was meaningful to downstream systems.
Beginning in this release, mismatched values are written out in CSV format as String values by default.
NOTE: The ability to write out mismatched values in CSV outputs is enabled by default in new flows and CSV publishing actions. For existing CSV outputs, the prior behavior is maintained.
NOTE: This capability applies only to CSV outputs at this time. In the future, it will be applied to other non-schematized outputs, such as JSON.
Tip: When visual profiling is enabled, you can still identify the values in the generated results that are mismatched for their column data types.
As needed, you can configure the ability to write out mismatches for CSV outputs for individual publishing actions. For more information, see File Settings.
Snowflake date publishing improvements
In prior releases, the
|D s webapp|
Beginning in this release, the
|D s webapp|
For more information, see Snowflake Data Type Conversions.
Data type inference and row split inference run on more data
When an dataset is imported into the
|D s webapp|
NOTE: The following is applied to datasets that do not contain schema information.
- Split row inference: Patterns in the data are used to determine the end of a row of data. When a larger volume of data is read, there should be more potential rows to review, resulting in better precision on where to split the data into separate rows in the application.
- Type inference: Patterns of data in the same column are used to determine the best
to assign to the imported dataset. A larger volume of data means that the application has more values for the same column from which to infer the appropriate data type.
D s item item data type
NOTE: An increased data volume should result in a more accurate split row and data type inferencing. For pre-existing datasets, this increased volume may result in changes to the row and column type definitions when a dataset is imported.
Tip: For datasets that are demarcated by quoted values, you may experience a change in how columns are typed.
If you notice unexpected changes in column data types or in row splits in your datasets:
- Type inference: You should move your recipe panel cursor to the top of the dataset to see if you must reassign data types.
- Split row inference: Create a new imported dataset, disabling type inference in the import settings. Check the
splitrowstransform to see if it is splitting the rows appropriately. For more information, see Import Data Page.
PII - Improved matching for social security numbers
NOTE: If you created recipe steps that forcibly change a column's data type from within the application to be different from the source data type of your relational source, you may need to revise these recipe steps or remove them altogether.
During publication, the
|D s also|