diff --git a/docs/StardustDocs/d.tree b/docs/StardustDocs/d.tree
index d0b561def0..e22a7c83f2 100644
--- a/docs/StardustDocs/d.tree
+++ b/docs/StardustDocs/d.tree
@@ -45,6 +45,7 @@
         <toc-element topic="schemasInheritance.md"/>
         <toc-element topic="Data-Schemas-In-Kotlin-Notebook.md"/>
         <toc-element topic="schemasImportOpenApiJupyter.md"/>
+        <toc-element topic="Data-Schemas-And-Extension-Properties-Troubleshooting.md"/>
         <toc-element topic="Gradle-Plugin.md">
             <toc-element topic="schemasGradle.md"/>
             <toc-element topic="schemasImportSqlGradle.md"/>
diff --git a/docs/StardustDocs/topics/extensionPropertiesApi.md b/docs/StardustDocs/topics/extensionPropertiesApi.md
index ece739a575..654b869286 100644
--- a/docs/StardustDocs/topics/extensionPropertiesApi.md
+++ b/docs/StardustDocs/topics/extensionPropertiesApi.md
@@ -291,3 +291,15 @@ However, you can work around this by casting back to the original schema:
 df.add("name") { "branchName" }
     .filter { it.cast<BranchData>().profit > 0 }
 ```
+
+## Troubleshooting 
+
+Sometimes you can get an exception with a message containing
+
+```plain text
+..exception in generated DataFrame extension property..
+```
+
+This may be caused by incompatible schema usage or incorrectly defined column types.
+
+See [](Data-Schemas-And-Extension-Properties-Troubleshooting.md) for more information.
diff --git a/docs/StardustDocs/topics/schemas/Data-Schemas-And-Extension-Properties-Troubleshooting.md b/docs/StardustDocs/topics/schemas/Data-Schemas-And-Extension-Properties-Troubleshooting.md
new file mode 100644
index 0000000000..7dcf3ff10d
--- /dev/null
+++ b/docs/StardustDocs/topics/schemas/Data-Schemas-And-Extension-Properties-Troubleshooting.md
@@ -0,0 +1,83 @@
+# Data Schemas and Extension Properties Troubleshooting
+
+Sometimes you can get an exception with a message containing
+
+```plain text
+..exception in generated DataFrame extension property..
+```
+
+This means there is a runtime error while accessing a [`DataFrame` extension property](extensionPropertiesApi.md),
+generated by the [Compiler Plugin](Compiler-Plugin.md) or in the [Kotlin Notebook](SetupKotlinNotebook.md).
+
+Such errors are caused by generating extension properties for data schemas that are not compatible with the 
+[`DataFrame`](DataFrame.md), [`DataRow`](DataRow.md), etc. 
+In most cases, the schema contains columns of the wrong names or types.
+
+```kotlin
+@DataSchema
+interface Schema {
+    val age: String
+}
+```
+
+```kotlin
+val df = dataFrameOf("age" to columnOf(17, 32, 26)).cast<Schema>()
+
+// Compiles correctly but fails on runtime
+df.filter { age > 20 }
+```
+
+## Possible reasons
+
+### Incompatible manually defined data schema
+
+If you define initad data schema manually,
+make sure your data schema is compatible with the [`DataFrame`](DataFrame.md).
+
+* Use [special methods](DataSchemaGenerationMethods.md) for generating a data schema code 
+instead of defining data schema manually.
+* Use [`.cast<Schema>()`](cast.md) with `verify=true` for verifying the `Schema` compatibility.
+
+### Incorrect column types after `DataFrame` creation
+
+Sometimes the runtime schema is wrong itself, 
+because column types differ from the actual column value types.
+This can happen when reading a DataFrame from files or databases.
+
+> Such cases are most probably bugs! Please report them on [GitHub Issues](https://github.com/Kotlin/dataframe/issues).
+{style="warning"}
+
+Possible workarounds:
+
+* Specify the correct type using [`.replace {}`](replace.md) and `ValueColumn.changeType()`:
+
+```kotlin
+df.replace { wrongTypeCol }.with { it.asValueColumn().changeType(typeOf<ActualType>) }
+```
+
+* Use [`.inferType { columns }`](inferType.md) to infer the correct types 
+for the selected columns from the actual values.
+**It can take a long time and use up a lot of resources for large dataframes!**
+
+#### Problems with type affinity in SQLite
+
+Because of [SQLite type affinity](https://sqlite.org/datatype3.html), 
+the column typed defined by JDBC may differ from the actual values in the column.
+This problem often occurs when reading data from an SQLite database with column of custom types.
+
+You can provide types for such columns manually:
+
+```Kotlin
+import org.jetbrains.kotlinx.dataframe.io.db.Sqlite
+import kotlin.reflect.typeOf
+
+val sqliteCustom = Sqlite.withCustomTypes(
+    mapOf(
+        "LONGVARCHAR" to typeOf<String>(),
+        "LONGINT" to typeOf<Long>()
+    )
+)
+val df = DataFrame.readSqlTable(
+    connection, "table_name", dbType = sqliteCustom
+)
+```