diff --git a/arrow-array/src/record_batch.rs b/arrow-array/src/record_batch.rs
index 8958ca6fae6..ec2b974fdf9 100644
--- a/arrow-array/src/record_batch.rs
+++ b/arrow-array/src/record_batch.rs
@@ -18,8 +18,10 @@
 //! A two-dimensional batch of column-oriented data with a defined
 //! [schema](arrow_schema::Schema).
 
+use crate::cast::AsArray;
 use crate::{new_empty_array, Array, ArrayRef, StructArray};
-use arrow_schema::{ArrowError, DataType, Field, Schema, SchemaBuilder, SchemaRef};
+use arrow_schema::{ArrowError, DataType, Field, FieldRef, Schema, SchemaBuilder, SchemaRef};
+use std::collections::VecDeque;
 use std::ops::Index;
 use std::sync::Arc;
 
@@ -394,6 +396,56 @@ impl RecordBatch {
         )
     }
 
+    /// Normalize a semi-structured [`RecordBatch`] into a flat table.
+    ///
+    /// If max_level is 0, normalizes all levels.
+    pub fn normalize(&self, separator: &str, mut max_level: usize) -> Result<Self, ArrowError> {
+        if max_level == 0 {
+            max_level = usize::MAX;
+        }
+        if self.num_rows() == 0 {
+            // No data, only need to normalize the schema
+            return Ok(Self::new_empty(Arc::new(
+                self.schema.normalize(separator, max_level)?,
+            )));
+        }
+        let mut queue: VecDeque<(usize, (ArrayRef, FieldRef))> = VecDeque::new();
+
+        for (c, f) in self.columns.iter().zip(self.schema.fields()) {
+            queue.push_back((0, ((*c).clone(), (*f).clone())));
+        }
+
+        let mut columns: Vec<ArrayRef> = Vec::new();
+        let mut fields: Vec<FieldRef> = Vec::new();
+
+        while let Some((depth, (c, f))) = queue.pop_front() {
+            if depth < max_level {
+                match f.data_type() {
+                    DataType::Struct(ff) => {
+                        // Need to zip these in reverse to maintain original order
+                        for (cff, fff) in c.as_struct().columns().iter().zip(ff.into_iter()).rev() {
+                            let new_key = format!("{}{}{}", f.name(), separator, fff.name());
+                            let updated_field = Field::new(
+                                new_key.as_str(),
+                                fff.data_type().clone(),
+                                fff.is_nullable(),
+                            );
+                            queue.push_front((depth + 1, (cff.clone(), Arc::new(updated_field))))
+                        }
+                    }
+                    _ => {
+                        columns.push(c);
+                        fields.push(f);
+                    }
+                }
+            } else {
+                columns.push(c);
+                fields.push(f);
+            }
+        }
+        RecordBatch::try_new(Arc::new(Schema::new(fields)), columns)
+    }
+
     /// Returns the number of columns in the record batch.
     ///
     /// # Example
@@ -1197,6 +1249,172 @@ mod tests {
         assert_ne!(batch1, batch2);
     }
 
+    #[test]
+    fn normalize() {
+        let animals: ArrayRef = Arc::new(StringArray::from(vec!["Parrot", ""]));
+        let n_legs: ArrayRef = Arc::new(Int64Array::from(vec![Some(2), Some(4)]));
+        let year: ArrayRef = Arc::new(Int64Array::from(vec![None, Some(2022)]));
+
+        let animals_field = Arc::new(Field::new("animals", DataType::Utf8, true));
+        let n_legs_field = Arc::new(Field::new("n_legs", DataType::Int64, true));
+        let year_field = Arc::new(Field::new("year", DataType::Int64, true));
+
+        let a = Arc::new(StructArray::from(vec![
+            (animals_field.clone(), Arc::new(animals.clone()) as ArrayRef),
+            (n_legs_field.clone(), Arc::new(n_legs.clone()) as ArrayRef),
+            (year_field.clone(), Arc::new(year.clone()) as ArrayRef),
+        ]));
+
+        let month = Arc::new(Int64Array::from(vec![Some(4), Some(6)]));
+
+        let schema = Schema::new(vec![
+            Field::new(
+                "a",
+                DataType::Struct(Fields::from(vec![animals_field, n_legs_field, year_field])),
+                false,
+            ),
+            Field::new("month", DataType::Int64, true),
+        ]);
+
+        let normalized = RecordBatch::try_new(Arc::new(schema), vec![a, month.clone()])
+            .expect("valid conversion")
+            .normalize(".", 0)
+            .expect("valid normalization");
+
+        let expected = RecordBatch::try_from_iter_with_nullable(vec![
+            ("a.animals", animals.clone(), true),
+            ("a.n_legs", n_legs.clone(), true),
+            ("a.year", year.clone(), true),
+            ("month", month.clone(), true),
+        ])
+        .expect("valid conversion");
+
+        assert_eq!(expected, normalized);
+    }
+
+    #[test]
+    fn normalize_nested() {
+        // Initialize schema
+        let a = Arc::new(Field::new("a", DataType::Int64, true));
+        let b = Arc::new(Field::new("b", DataType::Int64, false));
+        let c = Arc::new(Field::new("c", DataType::Int64, true));
+
+        let one = Arc::new(Field::new(
+            "1",
+            DataType::Struct(Fields::from(vec![a.clone(), b.clone(), c.clone()])),
+            false,
+        ));
+        let two = Arc::new(Field::new(
+            "2",
+            DataType::Struct(Fields::from(vec![a.clone(), b.clone(), c.clone()])),
+            true,
+        ));
+
+        let exclamation = Arc::new(Field::new(
+            "!",
+            DataType::Struct(Fields::from(vec![one.clone(), two.clone()])),
+            false,
+        ));
+
+        let schema = Schema::new(vec![exclamation.clone()]);
+
+        // Initialize fields
+        let a_field = Int64Array::from(vec![Some(0), Some(1)]);
+        let b_field = Int64Array::from(vec![Some(2), Some(3)]);
+        let c_field = Int64Array::from(vec![None, Some(4)]);
+
+        let one_field = StructArray::from(vec![
+            (a.clone(), Arc::new(a_field.clone()) as ArrayRef),
+            (b.clone(), Arc::new(b_field.clone()) as ArrayRef),
+            (c.clone(), Arc::new(c_field.clone()) as ArrayRef),
+        ]);
+        let two_field = StructArray::from(vec![
+            (a.clone(), Arc::new(a_field.clone()) as ArrayRef),
+            (b.clone(), Arc::new(b_field.clone()) as ArrayRef),
+            (c.clone(), Arc::new(c_field.clone()) as ArrayRef),
+        ]);
+
+        let exclamation_field = Arc::new(StructArray::from(vec![
+            (one.clone(), Arc::new(one_field) as ArrayRef),
+            (two.clone(), Arc::new(two_field) as ArrayRef),
+        ]));
+
+        // Normalize top level
+        let normalized =
+            RecordBatch::try_new(Arc::new(schema.clone()), vec![exclamation_field.clone()])
+                .expect("valid conversion")
+                .normalize(".", 1)
+                .expect("valid normalization");
+
+        let expected = RecordBatch::try_from_iter_with_nullable(vec![
+            (
+                "!.1",
+                Arc::new(StructArray::from(vec![
+                    (a.clone(), Arc::new(a_field.clone()) as ArrayRef),
+                    (b.clone(), Arc::new(b_field.clone()) as ArrayRef),
+                    (c.clone(), Arc::new(c_field.clone()) as ArrayRef),
+                ])) as ArrayRef,
+                false,
+            ),
+            (
+                "!.2",
+                Arc::new(StructArray::from(vec![
+                    (a.clone(), Arc::new(a_field.clone()) as ArrayRef),
+                    (b.clone(), Arc::new(b_field.clone()) as ArrayRef),
+                    (c.clone(), Arc::new(c_field.clone()) as ArrayRef),
+                ])) as ArrayRef,
+                true,
+            ),
+        ])
+        .expect("valid conversion");
+
+        assert_eq!(expected, normalized);
+
+        // Normalize all levels
+        let normalized = RecordBatch::try_new(Arc::new(schema), vec![exclamation_field])
+            .expect("valid conversion")
+            .normalize(".", 0)
+            .expect("valid normalization");
+
+        let expected = RecordBatch::try_from_iter_with_nullable(vec![
+            ("!.1.a", Arc::new(a_field.clone()) as ArrayRef, true),
+            ("!.1.b", Arc::new(b_field.clone()) as ArrayRef, false),
+            ("!.1.c", Arc::new(c_field.clone()) as ArrayRef, true),
+            ("!.2.a", Arc::new(a_field.clone()) as ArrayRef, true),
+            ("!.2.b", Arc::new(b_field.clone()) as ArrayRef, false),
+            ("!.2.c", Arc::new(c_field.clone()) as ArrayRef, true),
+        ])
+        .expect("valid conversion");
+
+        assert_eq!(expected, normalized);
+    }
+
+    #[test]
+    fn normalize_empty() {
+        let animals_field = Arc::new(Field::new("animals", DataType::Utf8, true));
+        let n_legs_field = Arc::new(Field::new("n_legs", DataType::Int64, true));
+        let year_field = Arc::new(Field::new("year", DataType::Int64, true));
+
+        let schema = Schema::new(vec![
+            Field::new(
+                "a",
+                DataType::Struct(Fields::from(vec![animals_field, n_legs_field, year_field])),
+                false,
+            ),
+            Field::new("month", DataType::Int64, true),
+        ]);
+
+        let normalized = RecordBatch::new_empty(Arc::new(schema.clone()))
+            .normalize(".", 0)
+            .expect("valid normalization");
+
+        let expected = RecordBatch::new_empty(Arc::new(
+            schema.normalize(".", 0).expect("valid normalization"),
+        ));
+
+        assert_eq!(expected, normalized);
+    }
+
     #[test]
     fn project() {
         let a: ArrayRef = Arc::new(Int32Array::from(vec![Some(1), None, Some(3)]));
diff --git a/arrow-schema/src/schema.rs b/arrow-schema/src/schema.rs
index c5c22b52713..ca0532cecdc 100644
--- a/arrow-schema/src/schema.rs
+++ b/arrow-schema/src/schema.rs
@@ -22,7 +22,7 @@ use std::sync::Arc;
 
 use crate::error::ArrowError;
 use crate::field::Field;
-use crate::{FieldRef, Fields};
+use crate::{DataType, FieldRef, Fields};
 
 /// A builder to facilitate building a [`Schema`] from iteratively from [`FieldRef`]
 #[derive(Debug, Default)]
@@ -413,6 +413,81 @@ impl Schema {
         &self.metadata
     }
 
+    /// Returns a new schema, normalized based on the max_level
+    /// This carries metadata from the parent schema over as well
+    pub fn normalize(&self, separator: &str, mut max_level: usize) -> Result<Self, ArrowError> {
+        if max_level == 0 {
+            max_level = usize::MAX;
+        }
+        let mut new_fields: Vec<FieldRef> = vec![];
+        for field in self.fields() {
+            match field.data_type() {
+                DataType::Struct(nested_fields) => {
+                    let field_name = field.name().as_str();
+                    new_fields = [
+                        new_fields,
+                        Self::normalizer(
+                            nested_fields.to_vec(),
+                            field_name,
+                            separator,
+                            max_level - 1,
+                        ),
+                    ]
+                    .concat();
+                }
+                _ => new_fields.push(Arc::new(Field::new(
+                    field.name(),
+                    field.data_type().clone(),
+                    field.is_nullable(),
+                ))),
+            };
+        }
+        Ok(Self::new_with_metadata(new_fields, self.metadata.clone()))
+    }
+
+    fn normalizer(
+        fields: Vec<FieldRef>,
+        key_string: &str,
+        separator: &str,
+        max_level: usize,
+    ) -> Vec<FieldRef> {
+        let mut new_fields: Vec<FieldRef> = vec![];
+        if max_level > 0 {
+            for field in fields {
+                match field.data_type() {
+                    DataType::Struct(nested_fields) => {
+                        let field_name = field.name().as_str();
+                        let new_key = format!("{key_string}{separator}{field_name}");
+                        new_fields = [
+                            new_fields,
+                            Self::normalizer(
+                                nested_fields.to_vec(),
+                                new_key.as_str(),
+                                separator,
+                                max_level - 1,
+                            ),
+                        ]
+                        .concat();
+                    }
+                    _ => new_fields.push(Arc::new(Field::new(
+                        format!("{key_string}{separator}{}", field.name()),
+                        field.data_type().clone(),
+                        field.is_nullable(),
+                    ))),
+                };
+            }
+        } else {
+            for field in fields {
+                new_fields.push(Arc::new(Field::new(
+                    format!("{key_string}{separator}{}", field.name()),
+                    field.data_type().clone(),
+                    field.is_nullable(),
+                )));
+            }
+        }
+        new_fields
+    }
+
     /// Look up a column by name and return a immutable reference to the column along with
     /// its index.
     pub fn column_with_name(&self, name: &str) -> Option<(usize, &Field)> {
@@ -697,6 +772,87 @@ mod tests {
         schema.index_of("nickname").unwrap();
     }
 
+    #[test]
+    fn normalize() {
+        let schema = Schema::new(vec![
+            Field::new(
+                "a",
+                DataType::Struct(Fields::from(vec![
+                    Arc::new(Field::new("animals", DataType::Utf8, true)),
+                    Arc::new(Field::new("n_legs", DataType::Int64, true)),
+                    Arc::new(Field::new("year", DataType::Int64, true)),
+                ])),
+                false,
+            ),
+            Field::new("month", DataType::Int64, true),
+        ])
+        .normalize(".", 0)
+        .expect("valid normalization");
+
+        let expected = Schema::new(vec![
+            Field::new("a.animals", DataType::Utf8, true),
+            Field::new("a.n_legs", DataType::Int64, true),
+            Field::new("a.year", DataType::Int64, true),
+            Field::new("month", DataType::Int64, true),
+        ]);
+
+        assert_eq!(schema, expected);
+    }
+
+    #[test]
+    fn normalize_nested() {
+        let a = Arc::new(Field::new("a", DataType::Utf8, true));
+        let b = Arc::new(Field::new("b", DataType::Int64, false));
+        let c = Arc::new(Field::new("c", DataType::Int64, true));
+
+        let d = Arc::new(Field::new("d", DataType::Utf8, true));
+        let e = Arc::new(Field::new("e", DataType::Int64, false));
+        let f = Arc::new(Field::new("f", DataType::Int64, true));
+
+        let one = Arc::new(Field::new(
+            "1",
+            DataType::Struct(Fields::from(vec![a.clone(), b.clone(), c.clone()])),
+            false,
+        ));
+        let two = Arc::new(Field::new(
+            "2",
+            DataType::Struct(Fields::from(vec![d.clone(), e.clone(), f.clone()])),
+            true,
+        ));
+
+        let exclamation = Arc::new(Field::new(
+            "!",
+            DataType::Struct(Fields::from(vec![one, two])),
+            false,
+        ));
+
+        let normalize_all = Schema::new(vec![exclamation.clone()])
+            .normalize(".", 0)
+            .expect("valid normalization");
+
+        let expected = Schema::new(vec![
+            Field::new("!.1.a", DataType::Utf8, true),
+            Field::new("!.1.b", DataType::Int64, false),
+            Field::new("!.1.c", DataType::Int64, true),
+            Field::new("!.2.d", DataType::Utf8, true),
+            Field::new("!.2.e", DataType::Int64, false),
+            Field::new("!.2.f", DataType::Int64, true),
+        ]);
+
+        assert_eq!(normalize_all, expected);
+
+        let normalize_depth_one = Schema::new(vec![exclamation])
+            .normalize(".", 1)
+            .expect("valid normalization");
+
+        let expected = Schema::new(vec![
+            Field::new("!.1", DataType::Struct(Fields::from(vec![a, b, c])), false),
+            Field::new("!.2", DataType::Struct(Fields::from(vec![d, e, f])), true),
+        ]);
+
+        assert_eq!(normalize_depth_one, expected);
+    }
+
     #[test]
     #[should_panic(
         expected = "Unable to get field named \\\"nickname\\\". Valid fields: [\\\"first_name\\\", \\\"last_name\\\", \\\"address\\\", \\\"interests\\\"]"