Databricks

Source and Destination Databases

Important

This connector can only be used as a source database. The generated data can be written to Local Filesystem, Azure Data Lake Storage (ADLS) or Amazon Simple Storage Service (S3) as Parquet files.

Before you begin

Before you begin, gather this connection information:

• Name of the server that hosts the database you want to connect to and port number

• The name of the database that you want to connect to

• HTTP path to the data source

• Personal Access Token

• In Databricks, find your cluster server hostname and HTTP path using the instructions in Construct the JDBC URL on the Databricks website.

Connect and set up the workspace

Launch Syntho and select Connect to a database, or Create workspace. Then select Databricks from Type under The connection details. For a complete list of data connections, click Type under The connection details. Then do the following:

1. Launch Syntho and select Connect to a database (or Create workspace).

2. Under The connection details, choose Databricks from the Type dropdown.

3. Fill in the required fields:

   • Server hostname → e.g. adb-1111111111111111.0.azuredatabricks.net

   • Catalog name → e.g. demo_catalog

   • Database name → e.g. marketing_db

   • HTTP Path → e.g. sql/protocolv1/o/1234567890123456/0000-111111-demo123

   • Port number → default is 443

   • Personal Access Token → (See Personal Access Tokens on the Databricks website for information on access tokens.)

4. Click Create Workspace to complete the setup. If Syntho can't make the connection, verify that your credentials are correct. If issues persist, your computer may not be able to locate the server. Contact your network administrator or database administrator for support.

Supported Databrick versions

The table below provides an overview of the supported Databricks versions and their corresponding Apache Spark versions.

Databricks Version	Spark Version
16.2	3.5.0
15.4 LTS	3.5.0
14.3 LTS	3.5.0

Note: Version 13 is no longer supported.

Supported data types

The following table summarizes the current support limitations for various data types when using connectors with Databricks. It indicates what's supported per generator type.

Supported data types

The following table summarizes the current support limitations for various data types when using connectors with Databricks. It indicates what's supported per generator type.

Data Type	AI-powered Generation	Mockers	Mask	Calculated Columns
INT	True	True	True	True
SMALLINT	True	True	True	True
TINYINT	True	True	True	True
BIGINT	True	True	True	True
DECIMAL	True	True	True	True
FLOAT	True	True	True	True
DOUBLE	True	True	True	True
STRING	True	True	True	True
BINARY	False	True*	True*	True*
BOOLEAN	False	True	False	True*
DATE	True	True	True	False
TIMESTAMP	True	True	True	False
TIMESTAMP_NTZ	True	True	True	False
ARRAY	False	True*	True*	True*
STRUCT	False	True	False	False
MAP	False	True*	True*	True*
VARIANT	False	True*	True*	True*
OBJECT	False	True*	True*	True*
ENUM	False	False	False	False

*Some data types are not actively supported; however, certain generators such as AI synthesize, mask, mockers, or calculated columns may still show 'True' for these fields. This means the generators can be applied, even though the types are not actively supported. Duplication is fully supported for these data types.

Limitations

• When entering database or schema names, use lowercase letters. Names containing capital letters must be entered in lowercase to ensure a proper connection.

• Schema, table and column names with single quotation marks (`) or backtick (') symbols are not supported.