Snowflake mappings that run on the Amazon EMR 6.1 cluster fail with the following error:

NoClassDefFoundError: scala/Product$class

Snowflake mappings that run on the Databricks Spark engine version 7.2 fail with the following error:

java.lang.NoSuchMethodError: scala.Predef$.refArrayOps

If the length of the SQL override in a Snowflake mapping exceeds 4000 characters, the mapping fails.

When you run a mapping on the Spark engine to write data to a Snowflake target and the target table and column names are not in uppercase letters, the mapping fails.

When you configure pushdown optimization in a mapping with the update as update, update else insert, or delete operation, the pushdown query is not generated.

The COPY command for external table mappings fails to run on the Spark engine.

When you run a Snowflake mapping on the Spark engine and the data contains case-sensitive tables, the mapping fails.

When you use Snowflake jars, version 2.11-2.4.3, the performance of the read operation of a Snowflake mapping on the Spark engine drops by 1.5X when compared to Snowflake jars of version 2.11-2.3.1.

When you run a mapping with source pushdown optimization enabled and the mapping includes a Sorter transformation, the mapping fails.

When you run a mapping on the Spark engine to write data to a Snowflake target, and the data contains the Binary data type, the mapping fails.

When you import Snowflake tables, the table type reflects as TABLE rather than EXTERNAL_TABLE in the package explorer.

When you run a mapping in the native environment to read large volumes of data from Snowflake, the mapping fails with an out-of-memory error.

When you run a mapping on the Spark engine to write data of the Time data type, the Data Integration Service does not write the data to the Snowflake table even though the mapping runs successfully.