diff --git a/common/utils/src/main/resources/error/error-conditions.json b/common/utils/src/main/resources/error/error-conditions.json
index 29eda228c2daa..0ebeea9aed8d2 100644
--- a/common/utils/src/main/resources/error/error-conditions.json
+++ b/common/utils/src/main/resources/error/error-conditions.json
@@ -6656,11 +6656,6 @@
       "Type <dataType> does not support ordered operations."
     ]
   },
-  "_LEGACY_ERROR_TEMP_2011" : {
-    "message" : [
-      "Unexpected data type <dataType>."
-    ]
-  },
   "_LEGACY_ERROR_TEMP_2013" : {
     "message" : [
       "Negative values found in <frequencyExpression>"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/CSVInferSchema.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/CSVInferSchema.scala
index 2c27da3cf6e15..5444ab6845867 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/CSVInferSchema.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/CSVInferSchema.scala
@@ -21,12 +21,12 @@ import java.util.Locale
 
 import scala.util.control.Exception.allCatch
 
+import org.apache.spark.SparkException
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.analysis.TypeCoercion
 import org.apache.spark.sql.catalyst.expressions.ExprUtils
 import org.apache.spark.sql.catalyst.util.{DateFormatter, TimestampFormatter}
 import org.apache.spark.sql.catalyst.util.LegacyDateFormats.FAST_DATE_FORMAT
-import org.apache.spark.sql.errors.QueryExecutionErrors
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.types._
 
@@ -138,7 +138,7 @@ class CSVInferSchema(val options: CSVOptions) extends Serializable {
         case BooleanType => tryParseBoolean(field)
         case StringType => StringType
         case other: DataType =>
-          throw QueryExecutionErrors.dataTypeUnexpectedError(other)
+          throw SparkException.internalError(s"Unexpected data type $other")
       }
       compatibleType(typeSoFar, typeElemInfer).getOrElse(StringType)
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala
index 4987e31b49911..8ad062ab0e2f9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala
@@ -21,6 +21,7 @@ import java.nio.ByteBuffer
 
 import com.google.common.primitives.{Doubles, Ints, Longs}
 
+import org.apache.spark.SparkException
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.{FunctionRegistry, TypeCheckResult}
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{DataTypeMismatch, TypeCheckSuccess}
@@ -32,7 +33,6 @@ import org.apache.spark.sql.catalyst.types.PhysicalNumericType
 import org.apache.spark.sql.catalyst.util.{ArrayData, GenericArrayData}
 import org.apache.spark.sql.catalyst.util.QuantileSummaries
 import org.apache.spark.sql.catalyst.util.QuantileSummaries.{defaultCompressThreshold, Stats}
-import org.apache.spark.sql.errors.QueryExecutionErrors
 import org.apache.spark.sql.types._
 import org.apache.spark.util.ArrayImplicits._
 
@@ -189,7 +189,7 @@ case class ApproximatePercentile(
           PhysicalNumericType.numeric(n)
             .toDouble(value.asInstanceOf[PhysicalNumericType#InternalType])
         case other: DataType =>
-          throw QueryExecutionErrors.dataTypeUnexpectedError(other)
+          throw SparkException.internalError(s"Unexpected data type $other")
       }
       buffer.add(doubleValue)
     }
@@ -214,7 +214,7 @@ case class ApproximatePercentile(
       case DoubleType => doubleResult
       case _: DecimalType => doubleResult.map(Decimal(_))
       case other: DataType =>
-        throw QueryExecutionErrors.dataTypeUnexpectedError(other)
+        throw SparkException.internalError(s"Unexpected data type $other")
     }
     if (result.length == 0) {
       null
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryExecutionErrors.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryExecutionErrors.scala
index 0b37cf951a29b..2ab86a5c5f03f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryExecutionErrors.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryExecutionErrors.scala
@@ -384,18 +384,6 @@ private[sql] object QueryExecutionErrors extends QueryErrorsBase with ExecutionE
       s"The aggregate window function ${toSQLId(funcName)} does not support merging.")
   }
 
-  def dataTypeUnexpectedError(dataType: DataType): SparkUnsupportedOperationException = {
-    new SparkUnsupportedOperationException(
-      errorClass = "_LEGACY_ERROR_TEMP_2011",
-      messageParameters = Map("dataType" -> dataType.catalogString))
-  }
-
-  def typeUnsupportedError(dataType: DataType): SparkIllegalArgumentException = {
-    new SparkIllegalArgumentException(
-      errorClass = "_LEGACY_ERROR_TEMP_2011",
-      messageParameters = Map("dataType" -> dataType.toString()))
-  }
-
   def negativeValueUnexpectedError(
       frequencyExpression : Expression): SparkIllegalArgumentException = {
     new SparkIllegalArgumentException(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
index 676a2ab64d0a3..ffdca65151052 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
@@ -29,7 +29,7 @@ import scala.util.control.NonFatal
 
 import org.apache.hadoop.fs.Path
 
-import org.apache.spark.SparkRuntimeException
+import org.apache.spark.{SparkException, SparkRuntimeException}
 import org.apache.spark.sql.catalyst.{InternalRow, SQLConfHelper}
 import org.apache.spark.sql.catalyst.analysis.TypeCoercion
 import org.apache.spark.sql.catalyst.catalog.CatalogTypes.TablePartitionSpec
@@ -550,7 +550,7 @@ object PartitioningUtils extends SQLConfHelper {
       Cast(Literal(unescapePathName(value)), it).eval()
     case BinaryType => value.getBytes()
     case BooleanType => value.toBoolean
-    case dt => throw QueryExecutionErrors.typeUnsupportedError(dt)
+    case dt => throw SparkException.internalError(s"Unsupported partition type: $dt")
   }
 
   def validatePartitionColumn(