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The CSV parser supports three modes when parsing records: PERMISSIVE, DROPMALFORMED, and FAILFAST. You can enable the rescued data column by setting the option rescuedDataColumn to a column name, such as _rescued_data with ("rescuedDataColumn", "_rescued_data").format("csv").load(). To remove the source file path from the rescued data column, you can set the SQL configuration (".filePath.enabled", "false"). The rescued data column is returned as a JSON blob containing the columns that were rescued, and the source file path of the record (the source file path is available in Databricks Runtime 8.3 and above). The rescued data column contains any data that wasn’t parsed, either because it was missing from the given schema, or because there was a type mismatch, or because the casing of the column in the record or file didn’t match with that in the schema. The rescued data column ensures that you never lose or miss out on data during ETL. This feature is supported in Databricks Runtime 8.3 (Unsupported) and above.
#JSON COMPARE CSV GENERATOR#
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Using this primary key, it can figure out modifications. Specify comma separated positions if the table has a compound key.
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The additions.csv can be used to create an insert.sql and with the modifications.csv an update.sql data migration. If you receive the changes as another database dump as csv, this tool can be used to figure out what are the additions and modifications to the original database dump. Cases where you have a base database dump as csv.