diff --git a/arrow-data-source/common/src/main/scala/org/apache/spark/sql/execution/datasources/v2/arrow/SparkSchemaUtils.scala b/arrow-data-source/common/src/main/scala/org/apache/spark/sql/execution/datasources/v2/arrow/SparkSchemaUtils.scala
index f7ba1029d..efbfd9261 100644
--- a/arrow-data-source/common/src/main/scala/org/apache/spark/sql/execution/datasources/v2/arrow/SparkSchemaUtils.scala
+++ b/arrow-data-source/common/src/main/scala/org/apache/spark/sql/execution/datasources/v2/arrow/SparkSchemaUtils.scala
@@ -20,10 +20,10 @@ package org.apache.spark.sql.execution.datasources.v2.arrow
 import java.util.Objects
 import java.util.TimeZone
 
-import org.apache.arrow.vector.types.pojo.Schema
+import org.apache.arrow.vector.types.pojo.{Field, Schema}
 
 import org.apache.spark.sql.internal.SQLConf
-import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.types.{DataType, StructType}
 import org.apache.spark.sql.util.ArrowUtils
 
 object SparkSchemaUtils {
@@ -36,6 +36,11 @@ object SparkSchemaUtils {
     ArrowUtils.toArrowSchema(schema, timeZoneId)
   }
 
+  def toArrowField(
+      name: String, dt: DataType, nullable: Boolean, timeZoneId: String): Field = {
+    ArrowUtils.toArrowField(name, dt, nullable, timeZoneId)
+  }
+
   @deprecated  // experimental
   def getGandivaCompatibleTimeZoneID(): String = {
     val zone = SQLConf.get.sessionLocalTimeZone
diff --git a/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/arrow/ArrowFileFormat.scala b/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/arrow/ArrowFileFormat.scala
index 1f172b043..155ecc21c 100644
--- a/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/arrow/ArrowFileFormat.scala
+++ b/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/arrow/ArrowFileFormat.scala
@@ -20,6 +20,7 @@ package com.intel.oap.spark.sql.execution.datasources.arrow
 import java.net.URLDecoder
 
 import scala.collection.JavaConverters._
+import scala.collection.mutable
 
 import com.intel.oap.spark.sql.ArrowWriteExtension.FakeRow
 import com.intel.oap.spark.sql.ArrowWriteQueue
@@ -27,6 +28,7 @@ import com.intel.oap.spark.sql.execution.datasources.v2.arrow.{ArrowFilters, Arr
 import com.intel.oap.spark.sql.execution.datasources.v2.arrow.ArrowSQLConf._
 import com.intel.oap.vectorized.ArrowWritableColumnVector
 import org.apache.arrow.dataset.scanner.ScanOptions
+import org.apache.arrow.vector.types.pojo.Schema
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileStatus, Path}
 import org.apache.hadoop.mapreduce.Job
@@ -117,6 +119,7 @@ class ArrowFileFormat extends FileFormat with DataSourceRegister with Serializab
     val sqlConf = sparkSession.sessionState.conf;
     val batchSize = sqlConf.parquetVectorizedReaderBatchSize
     val enableFilterPushDown = sqlConf.arrowFilterPushDown
+    val caseSensitive = sqlConf.caseSensitiveAnalysis
 
     (file: PartitionedFile) => {
       val factory = ArrowUtils.makeArrowDiscovery(
@@ -126,16 +129,34 @@ class ArrowFileFormat extends FileFormat with DataSourceRegister with Serializab
             options.asJava).asScala.toMap))
 
       // todo predicate validation / pushdown
-      val dataset = factory.finish(ArrowUtils.toArrowSchema(requiredSchema));
+      val parquetFileFields = factory.inspect().getFields.asScala
+      val caseInsensitiveFieldMap = mutable.Map[String, String]()
+      val requiredFields = if (sqlConf.caseSensitiveAnalysis) {
+        new Schema(requiredSchema.map { field =>
+          parquetFileFields.find(_.getName.equals(field.name))
+            .getOrElse(ArrowUtils.toArrowField(field))
+        }.asJava)
+      } else {
+        new Schema(requiredSchema.map { readField =>
+          parquetFileFields.find(_.getName.equalsIgnoreCase(readField.name))
+            .map{ field =>
+              caseInsensitiveFieldMap += (readField.name -> field.getName)
+              field
+            }.getOrElse(ArrowUtils.toArrowField(readField))
+        }.asJava)
+      }
+      val dataset = factory.finish(requiredFields)
 
       val filter = if (enableFilterPushDown) {
-        ArrowFilters.translateFilters(filters)
+        ArrowFilters.translateFilters(filters, caseInsensitiveFieldMap.toMap)
       } else {
         org.apache.arrow.dataset.filter.Filter.EMPTY
       }
 
-      val scanOptions = new ScanOptions(requiredSchema.map(f => f.name).toArray,
-        filter, batchSize)
+      val scanOptions = new ScanOptions(
+        requiredFields.getFields.asScala.map(f => f.getName).toArray,
+        filter,
+        batchSize)
       val scanner = dataset.newScan(scanOptions)
 
       val taskList = scanner
diff --git a/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/v2/arrow/ArrowFilters.scala b/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/v2/arrow/ArrowFilters.scala
index f33c7995a..0bcfd3812 100644
--- a/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/v2/arrow/ArrowFilters.scala
+++ b/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/v2/arrow/ArrowFilters.scala
@@ -20,6 +20,7 @@ package com.intel.oap.spark.sql.execution.datasources.v2.arrow
 import org.apache.arrow.dataset.DatasetTypes
 import org.apache.arrow.dataset.DatasetTypes.TreeNode
 import org.apache.arrow.dataset.filter.FilterImpl
+import org.apache.arrow.vector.types.pojo.Field
 
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.StructType
@@ -56,9 +57,11 @@ object ArrowFilters {
     false
   }
 
-  def translateFilters(pushedFilters: Seq[Filter]): org.apache.arrow.dataset.filter.Filter = {
+  def translateFilters(
+      pushedFilters: Seq[Filter],
+      caseInsensitiveFieldMap: Map[String, String]): org.apache.arrow.dataset.filter.Filter = {
     val node = pushedFilters
-      .flatMap(translateFilter)
+      .flatMap(filter => translateFilter(filter, caseInsensitiveFieldMap))
       .reduceOption((t1: TreeNode, t2: TreeNode) => {
         DatasetTypes.TreeNode.newBuilder.setAndNode(
           DatasetTypes.AndNode.newBuilder()
@@ -100,28 +103,35 @@ object ArrowFilters {
     }
   }
 
-  private def translateFilter(pushedFilter: Filter): Option[TreeNode] = {
+  private def translateFilter(
+      pushedFilter: Filter,
+      caseInsensitiveFieldMap: Map[String, String]): Option[TreeNode] = {
     pushedFilter match {
       case EqualTo(attribute, value) =>
-        createComparisonNode("equal", attribute, value)
+        createComparisonNode(
+          "equal", caseInsensitiveFieldMap.getOrElse(attribute, attribute), value)
       case GreaterThan(attribute, value) =>
-        createComparisonNode("greater", attribute, value)
+        createComparisonNode(
+          "greater", caseInsensitiveFieldMap.getOrElse(attribute, attribute), value)
       case GreaterThanOrEqual(attribute, value) =>
-        createComparisonNode("greater_equal", attribute, value)
+        createComparisonNode(
+          "greater_equal", caseInsensitiveFieldMap.getOrElse(attribute, attribute), value)
       case LessThan(attribute, value) =>
-        createComparisonNode("less", attribute, value)
+        createComparisonNode(
+          "less", caseInsensitiveFieldMap.getOrElse(attribute, attribute), value)
       case LessThanOrEqual(attribute, value) =>
-        createComparisonNode("less_equal", attribute, value)
+        createComparisonNode(
+          "less_equal", caseInsensitiveFieldMap.getOrElse(attribute, attribute), value)
       case Not(child) =>
-        createNotNode(child)
+        createNotNode(child, caseInsensitiveFieldMap)
       case And(left, right) =>
-        createAndNode(left, right)
+        createAndNode(left, right, caseInsensitiveFieldMap)
       case Or(left, right) =>
-        createOrNode(left, right)
+        createOrNode(left, right, caseInsensitiveFieldMap)
       case IsNotNull(attribute) =>
-        createIsNotNullNode(attribute)
+        createIsNotNullNode(caseInsensitiveFieldMap.getOrElse(attribute, attribute))
       case IsNull(attribute) =>
-        createIsNullNode(attribute)
+        createIsNullNode(caseInsensitiveFieldMap.getOrElse(attribute, attribute))
       case _ => None // fixme complete this
     }
   }
@@ -145,8 +155,10 @@ object ArrowFilters {
     }
   }
 
-  def createNotNode(child: Filter): Option[TreeNode] = {
-    val translatedChild = translateFilter(child)
+  def createNotNode(
+      child: Filter,
+      caseInsensitiveFieldMap: Map[String, String]): Option[TreeNode] = {
+    val translatedChild = translateFilter(child, caseInsensitiveFieldMap)
     if (translatedChild.isEmpty) {
       return None
     }
@@ -176,9 +188,12 @@ object ArrowFilters {
                 .build()).build()).build()).build())
   }
 
-  def createAndNode(left: Filter, right: Filter): Option[TreeNode] = {
-    val translatedLeft = translateFilter(left)
-    val translatedRight = translateFilter(right)
+  def createAndNode(
+      left: Filter,
+      right: Filter,
+      caseInsensitiveFieldMap: Map[String, String]): Option[TreeNode] = {
+    val translatedLeft = translateFilter(left, caseInsensitiveFieldMap)
+    val translatedRight = translateFilter(right, caseInsensitiveFieldMap)
     if (translatedLeft.isEmpty || translatedRight.isEmpty) {
       return None
     }
@@ -190,9 +205,12 @@ object ArrowFilters {
       .build())
   }
 
-  def createOrNode(left: Filter, right: Filter): Option[TreeNode] = {
-    val translatedLeft = translateFilter(left)
-    val translatedRight = translateFilter(right)
+  def createOrNode(
+      left: Filter,
+      right: Filter,
+      caseInsensitiveFieldMap: Map[String, String]): Option[TreeNode] = {
+    val translatedLeft = translateFilter(left, caseInsensitiveFieldMap)
+    val translatedRight = translateFilter(right, caseInsensitiveFieldMap)
     if (translatedLeft.isEmpty || translatedRight.isEmpty) {
       return None
     }
diff --git a/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/v2/arrow/ArrowPartitionReaderFactory.scala b/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/v2/arrow/ArrowPartitionReaderFactory.scala
index 1e443a9d2..0ff3a2d56 100644
--- a/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/v2/arrow/ArrowPartitionReaderFactory.scala
+++ b/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/v2/arrow/ArrowPartitionReaderFactory.scala
@@ -19,10 +19,12 @@ package com.intel.oap.spark.sql.execution.datasources.v2.arrow
 import java.net.URLDecoder
 
 import scala.collection.JavaConverters._
+import scala.collection.mutable
 
 import com.intel.oap.spark.sql.execution.datasources.v2.arrow.ArrowPartitionReaderFactory.ColumnarBatchRetainer
 import com.intel.oap.spark.sql.execution.datasources.v2.arrow.ArrowSQLConf._
 import org.apache.arrow.dataset.scanner.ScanOptions
+import org.apache.arrow.vector.types.pojo.Schema
 
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.sql.catalyst.InternalRow
@@ -59,9 +61,27 @@ case class ArrowPartitionReaderFactory(
     val path = partitionedFile.filePath
     val factory = ArrowUtils.makeArrowDiscovery(URLDecoder.decode(path, "UTF-8"),
       partitionedFile.start, partitionedFile.length, options)
-    val dataset = factory.finish(ArrowUtils.toArrowSchema(readDataSchema))
+    val parquetFileFields = factory.inspect().getFields.asScala
+    val caseInsensitiveFieldMap = mutable.Map[String, String]()
+    val requiredFields = if (sqlConf.caseSensitiveAnalysis) {
+      new Schema(readDataSchema.map { field =>
+        parquetFileFields.find(_.getName.equals(field.name))
+          .getOrElse(ArrowUtils.toArrowField(field))
+      }.asJava)
+    } else {
+      new Schema(readDataSchema.map { readField =>
+        parquetFileFields.find(_.getName.equalsIgnoreCase(readField.name))
+          .map{ field =>
+            caseInsensitiveFieldMap += (readField.name -> field.getName)
+            field
+          }.getOrElse(ArrowUtils.toArrowField(readField))
+      }.asJava)
+    }
+    val dataset = factory.finish(requiredFields)
     val filter = if (enableFilterPushDown) {
-      ArrowFilters.translateFilters(ArrowFilters.pruneWithSchema(pushedFilters, readDataSchema))
+      ArrowFilters.translateFilters(
+        ArrowFilters.pruneWithSchema(pushedFilters, readDataSchema),
+        caseInsensitiveFieldMap.toMap)
     } else {
       org.apache.arrow.dataset.filter.Filter.EMPTY
     }
diff --git a/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/v2/arrow/ArrowUtils.scala b/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/v2/arrow/ArrowUtils.scala
index c30571b9e..5603326f3 100644
--- a/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/v2/arrow/ArrowUtils.scala
+++ b/arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/v2/arrow/ArrowUtils.scala
@@ -26,7 +26,7 @@ import scala.collection.JavaConverters._
 import com.intel.oap.vectorized.{ArrowColumnVectorUtils, ArrowWritableColumnVector}
 import org.apache.arrow.dataset.file.FileSystemDatasetFactory
 import org.apache.arrow.vector.ipc.message.ArrowRecordBatch
-import org.apache.arrow.vector.types.pojo.Schema
+import org.apache.arrow.vector.types.pojo.{Field, Schema}
 import org.apache.hadoop.fs.FileStatus
 
 import org.apache.spark.sql.catalyst.InternalRow
@@ -34,10 +34,9 @@ import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.execution.datasources.v2.arrow.{SparkMemoryUtils, SparkSchemaUtils}
 import org.apache.spark.sql.execution.vectorized.ColumnVectorUtils
 import org.apache.spark.sql.internal.SQLConf
-import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.types.{StructField, StructType}
 import org.apache.spark.sql.util.CaseInsensitiveStringMap
-import org.apache.spark.sql.vectorized.ColumnVector
-import org.apache.spark.sql.vectorized.ColumnarBatch
+import org.apache.spark.sql.vectorized.{ColumnarBatch, ColumnVector}
 
 object ArrowUtils {
 
@@ -89,6 +88,11 @@ object ArrowUtils {
     SparkSchemaUtils.toArrowSchema(t, SparkSchemaUtils.getLocalTimezoneID())
   }
 
+  def toArrowField(t: StructField): Field = {
+    SparkSchemaUtils.toArrowField(
+      t.name, t.dataType, t.nullable, SparkSchemaUtils.getLocalTimezoneID())
+  }
+
   def loadBatch(input: ArrowRecordBatch, partitionValues: InternalRow,
                   partitionSchema: StructType, dataSchema: StructType): ColumnarBatch = {
     val rowCount: Int = input.getLength
diff --git a/arrow-data-source/standard/src/test/scala/com/intel/oap/spark/sql/execution/datasources/arrow/ArrowDataSourceTest.scala b/arrow-data-source/standard/src/test/scala/com/intel/oap/spark/sql/execution/datasources/arrow/ArrowDataSourceTest.scala
index 9896ac1b4..c6c8ebe7f 100644
--- a/arrow-data-source/standard/src/test/scala/com/intel/oap/spark/sql/execution/datasources/arrow/ArrowDataSourceTest.scala
+++ b/arrow-data-source/standard/src/test/scala/com/intel/oap/spark/sql/execution/datasources/arrow/ArrowDataSourceTest.scala
@@ -286,6 +286,36 @@ class ArrowDataSourceTest extends QueryTest with SharedSparkSession {
 
   }
 
+  test("read and write with case sensitive or insensitive") {
+    val caseSensitiveAnalysisEnabled = Seq[Boolean](true, false)
+    val v1SourceList = Seq[String]("", "arrow")
+    caseSensitiveAnalysisEnabled.foreach{ caseSensitiveAnalysis =>
+      v1SourceList.foreach{v1Source =>
+        withSQLConf(
+          SQLConf.CASE_SENSITIVE.key -> caseSensitiveAnalysis.toString,
+          SQLConf.USE_V1_SOURCE_LIST.key -> v1Source) {
+          withTempPath { tempPath =>
+            spark.range(0, 100)
+              .withColumnRenamed("id", "Id")
+              .write
+              .mode("overwrite")
+              .arrow(tempPath.getPath)
+            val selectColName = if (caseSensitiveAnalysis) {
+              "Id"
+            } else {
+              "id"
+            }
+            val df = spark.read
+              .schema(s"$selectColName long")
+              .arrow(tempPath.getPath)
+              .filter(s"$selectColName <= 2")
+            checkAnswer(df, Row(0) :: Row(1) :: Row(2) :: Nil)
+          }
+        }
+      }
+    }
+  }
+
   test("file descriptor leak") {
     val path = ArrowDataSourceTest.locateResourcePath(parquetFile1)
     val frame = spark.read