diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 5ad2caba07fc..2981e391c043 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -26,7 +26,7 @@ import org.apache.hadoop.fs.Path
 
 import org.apache.spark.SparkException
 import org.apache.spark.internal.Logging
-import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.{AnalysisException, SparkSession}
 import org.apache.spark.sql.catalyst.{QualifiedTableName, TableIdentifier}
 import org.apache.spark.sql.catalyst.catalog._
 import org.apache.spark.sql.catalyst.plans.logical._
@@ -257,8 +257,20 @@ private[hive] class HiveMetastoreCatalog(sparkSession: SparkSession) extends Log
     }
     // The inferred schema may have different field names as the table schema, we should respect
     // it, but also respect the exprId in table relation output.
-    assert(result.output.length == relation.output.length &&
-      result.output.zip(relation.output).forall { case (a1, a2) => a1.dataType == a2.dataType })
+    if (result.output.length != relation.output.length) {
+      throw new AnalysisException(
+        s"Converted table has ${result.output.length} columns, " +
+        s"but source Hive table has ${relation.output.length} columns. " +
+        s"Set ${HiveUtils.CONVERT_METASTORE_PARQUET.key} to false, " +
+        s"or recreate table ${relation.tableMeta.identifier} to workaround.")
+    }
+    if (!result.output.zip(relation.output).forall {
+          case (a1, a2) => a1.dataType == a2.dataType }) {
+      throw new AnalysisException(
+        s"Column in converted table has different data type with source Hive table's. " +
+          s"Set ${HiveUtils.CONVERT_METASTORE_PARQUET.key} to false, " +
+          s"or recreate table ${relation.tableMeta.identifier} to workaround.")
+    }
     val newOutput = result.output.zip(relation.output).map {
       case (a1, a2) => a1.withExprId(a2.exprId)
     }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
index 0e45e18c4b17..9f2906df19bf 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.hive
 
-import org.apache.spark.sql.{QueryTest, Row, SaveMode}
+import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SaveMode}
 import org.apache.spark.sql.catalyst.{AliasIdentifier, TableIdentifier}
 import org.apache.spark.sql.catalyst.catalog.CatalogTableType
 import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
@@ -358,4 +358,24 @@ class DataSourceWithHiveMetastoreCatalogSuite
         Seq(table("src").count().toString))
     }
   }
+
+  test("SPARK-29869: Fix convertToLogicalRelation throws unclear AssertionError") {
+    withTempPath(dir => {
+      val baseDir = s"${dir.getCanonicalFile.toURI.toString}/non_partition_table"
+      val partitionLikeDir = s"$baseDir/dt=20191113"
+      spark.range(3).selectExpr("id").write.parquet(partitionLikeDir)
+      withTable("non_partition_table") {
+        withSQLConf(HiveUtils.CONVERT_METASTORE_PARQUET.key -> "true") {
+          spark.sql(
+            s"""
+               |CREATE TABLE non_partition_table (id bigint)
+               |STORED AS PARQUET LOCATION '$baseDir'
+               |""".stripMargin)
+          val e = intercept[AnalysisException](
+            spark.table("non_partition_table")).getMessage
+          assert(e.contains("Converted table has 2 columns, but source Hive table has 1 columns."))
+        }
+      }
+    })
+  }
 }