Data Source Support

Oracle Connector

Oracle Database (commonly referred to as Oracle RDBMS or simply as Oracle) is a multi-model database management system produced and marketed by Oracle Corporation. The following table lists the versions that have been tested in the lab setup:

Supported Data Types

The following are the different data types that are tested in our lab setup:

• VARCHAR

• VARCHAR2

• NUMBER

• FLOAT

• DATE

• TIMESTAMP(default)

• CLOB

• BLOB(with text)

Property values

For default values, see Configuration settings .

MS SQL Connector

Supported versions

Microsoft SQL Server 2019

Supported data types

The following are the different data types that are tested in our lab setup:

• VARCHAR

• CHAR

• DATETIME

• INT

• TEXT

• XML (only unload/load))

• VARBINARY (only unload/load)

• SMALLINT

• SMALLMONEY

• MONEY

• BIGINT

• NVARCHAR

• TINYINT

• NUMERIC(X,Y)

• DECIMAL(X,Y)

• FLOAT

• NCHAR

• BIT

• NTEXT

• MONEY

Property Values

For default values, see Configuration settings .

Known Limitations

• If the applied algorithm's produced mask data exceeds the corresponding target table columns datatype's max value range, then job execution will fail in load service.

• Schemas, tables, and column names having special characters are not supported.

• Masking of columns with VARBINARY datatype is not supported.

• Hyperscale Compliance can mask up to a maximum 1000 tables in a single job.

Delimited Files Connector

The connector can be used to mask large delimited files. The delimited unload service splits the large files into smaller chunks and passes them onto the masking service. After the masking is completed, the files are sent to the load service which joins back the split files (the end user also has a choice to disable the join operation).  

Pre-requisites

• The source and target (NFS) locations have to be mounted onto the docker containers of unload and load service. Please note that the locations on the containers are what needs to be used when creating the connector-info’s using the controller. 

  # As an example
  unload-service:
       image: delphix-delimited-unload-service-app:${VERSION}
       ...
       volumes:
            ...
       - /path/to/nfs/mounted/source1/files:/mnt/source1
       - /path/to/nfs/mounted/source2/files:/mnt/source2
  ...
  load-service:
       image: delphix-delimited-load-service-app:${VERSION}
       ...
       volumes:
            ...
       - /path/to/nfs/mounted/target1/files:/mnt/target1
       - /path/to/nfs/mounted/target2/files:/mnt/target2
  
  

Property values

For default values, see Configuration settings.

Supported data types

The following are the different data types that are tested in our lab setup:

• String/Text

• Double

  • Columns with values such as 36377974237282886994505 get converted to type double with value 3.637e22, inherently by PyArrow. In order to not have any loss of data, the Delimited Files Connector converts all double interpretations as strings.

  • In this case, 36377974237282886994505 will be converted to "36377974237282886994505".

• Int64

  • Columns with values such as M get inferred as type Int64, but the conversion fails for the above value. 

  • In order to mitigate the same, all Int64 types are converted to type String. In this case, 00009435304391722556805 will be converted to "00009435304391722556805".

• Timestamp

Known Limitations

The backend technology used to perform the split and join operations is PyArrow, which comes with certain limitations:

1. It supports only single-character delimiter.

2. The end-of-record character can only be `\n`, `\r`, or `\r\n`.

The output files will have all string types quoted with double quotes (`”`) only.