You cannot read from or write to tables where the table name exceeds 128 characters. The Integration Service fails to validate the data and an error occurs.

When you specify a native filter, enclose the table name and column name in the filter condition in quotes. For example,

"Supplier".s_suppkey<=5

When you run a JDBC V2 mapping to read or write data that contains time(4), time(5), and time(6) data types, the data is truncated beyond precision 3.

You cannot run mappings to read from or write to SAP HANA Database in the native environment.

SAP HANA Database field names with the aggregate function do not display correctly in the Designer.

Do not map the parameter field to the target in the mapping.

When you configure a filter, use only the native expression.

You cannot create dynamic mappings and use the

Create Target

option for SAP HANA Database.

When you run a mapping enabled to create a new Azure SQL Database target at runtime and the source data contains the Time data type, the Integration Service writes the date time value only until microseconds.

By default, the Target Schema Strategy is set to

RETAIN - Load into existing table schema

. You must manually select the

Target Schema Strategy

as

CREATE - Create target if it does not exist

in the Advanced properties in a Write operation for the

Create Target

option to work.

The CREATE TABLE DDL query that generates in the ANSI SQL-92 generic data type format might not run on all databases as the target database might not support the data type or data length. In this case, you must create the table manually and use it as the target in the mapping.

The option to edit the target metadata is subject to the target database support. When unsupported, you can see an error or warning message in the session logs.

Ensure to set the correct field precision and scale depending on the type of JDBC V2 database that you want to access.

When the database subtype used in the JDBC V2 connection is Others, you can write data that contains the BigInt data type only if that database supports the Numeric data type.

When you write to a PostgreSQL target and the source data contains the Time data type, the mapping runs successfully but the time value in microseconds is truncated to milliseconds.

When you write data that contains the Float, Double, Number, and Real data types to an Azure SQL Database target, the mapping fails. You must manually change the data type in the physical data object.

The Date and Time data type is converted to the Timestamp data type.

A JDBC V2 mapping that runs on the Spark engine to write Unicode characters to the target does not display the statistics. To make the summary statistics available on the

Monitoring

tab, before you run the mapping, set the spark.driver.extraJavaOptions and spark.executor.extraJavaOptions Spark advanced properties value to

-Dfile.encoding=UTF-8

in the Spark configuration section in the Hadoop connection.