Release Notes (10.4.1.3)

Release Notes (10.4.1.3)

Third-Party Fixed Issues (10.4.1.3)

Third-Party Fixed Issues (10.4.1.3)

The following table describes fixed issues:
Bug
Description
BDM-35570
When the Spark engine runs a mapping on an Amazon EMR 6.0 cluster fails with an error like:
org.apache.spark.sql.AnalysisException: Column <list of columns> are ambiguous. It's probably because you joined several Datasets together, and some of these Datasets are the same. This column points to one of the Datasets but Spark is unable to figure out which one. Please alias the Datasets with different names via `Dataset.as` before joining them, and specify the column using qualified name, e.g. `df.as("a").join(df.as("b"), $"a.id" > $"b.id")`. You can also set spark.sql.analyzer.failAmbiguousSelfJoin to false to disable this check.
Apache ticket number: SPARK-32551
OCON-25411
When you use the Cloudera Connector Powered by Teradata, Sqoop mappings that access the Teradata database fails on the Cloudera Data Platform 7.1 cluster.
Cloudera ticket reference number: 690026 .
EIC-48871
The Microsoft SQL Server resource fails to extract the Type object, if the object is created as both Type and Table in the Microsoft SQL Server data source.
MITI ticket reference number: INFAEDC-1564


Updated February 02, 2021