Databricks Tables Data Type Conversions

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NOTE: The Alteryx® data types listed in this page reflect the raw data type of the converted column. Depending on the contents of the column, the Transformer Page may re-infer a different data type, when a dataset using this type of source is loaded.

Access/Read

When a Databricks Tables data type is imported, its JDBC data type is remapped according to the following table.

Tip: Data precision may be lost during conversion. You may want to generate min and max values and compute significant digits for values in your Hive tables and then compute the same in the Designer Cloud application.

Source Data Type	Supported?	Alteryx Data Type	Notes
array	Y	Array
bigint	Y	Integer	NOTE: The Designer Cloud Powered by Trifacta platform may infer bigint columns containing very large or very small values as String data type.
binary	Y	String
boolean	Y	Bool
char	Y	String
date	Y	Datetime
decimal	Y	Decimal
double	Y	Decimal
float	Y	Decimal	NOTE: On import, some float columns may be interpreted as Integer data type in the Designer Cloud Powered by Trifacta platform. To fix, you can explicitly set the column's data type to Decimal in the Transformer page.
int	Y	Integer
map	Y	Object
smallint	Y	Integer
string	Y	String
struct	Y	Object
timestamp	Y	Datetime
tinyint	Y	Integer
uniontype	N
varchar	Y	String

Write/Publish

Create new table

Alteryx Data Type	Databricks Tables Data Type	Notes
String	string
Integer	bigint	NOTE: The Designer Cloud Powered by Trifacta platform may infer Integer columns containing very large or very small values as String data type. Before you publish, you should verify that your columns containing extreme values are interpreted as Integer type. You can import a target schema to assist in lining up your columns with the expected target. For more information, see Overview of RapidTarget.
Decimal	double
Bool	boolean
Datetime	Timestamp/string (see Notes on Datetime columns below)	Target data type is based on the underlying data. Time zone information is retained.
Object	string
Array	string

Append to existing table

If you are publishing to a pre-existing table, the following data type conversions apply:

Columns: Alteryx data types
Rows: Target table data types

In any table cell, a Y indicates that the append operation for that data type mapping is supported.

NOTE: You cannot append to Databricks Tables map and array column types from Alteryx columns of Map and Array type, even if you imported data from this source.

	String	Integer	Datetime	Bool	Decimal	Map	Array	Out of Range error
CHAR	Y	Y	Y	Y	Y	Y	Y
VARCHAR	Y	Y	Y	Y	Y	Y	Y
STRING	Y	Y	Y	Y	Y	Y	Y
INT		Y						NULL
BIGINT		Y						n/a
TINYINT								NULL
SMALLINT								NULL
DECIMAL		Y			Y			NULL
DOUBLE		Y			Y			n/a
FLOAT					Y			NULL
TIMESTAMP			Y
BOOLEAN				Y

Notes on Datetime columns

Run Job

Columns in new tables created for output of Datetime columns are written with the Databricks Tables timestamp data type. These columns can be appended.

A single job cannot write Datetime values to one table as String type and to another table as Timestamp type. This type of job should be split into multiple types. The table schemas may require modification.

The above issue may appear as the following error when executing the job:
```
Unable to publish due to datetime data type conflict in column XXXX
```

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