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diff --git a/Cargo.toml b/Cargo.toml
new file mode 100644
index 00000000..324f1613
--- /dev/null
+++ b/Cargo.toml
@@ -0,0 +1,22 @@
+[package]
+name = "xarray_sql"
+version = "0.1.0"
+authors = ["Alex Merose"]
+edition = "2021"
+exclude = ["perf_tests/*"]
+
+[dependencies]
+arrow = { version = "55.2.0", features = ["pyarrow"] }
+datafusion = { version = "49.0.0" }
+datafusion-ffi = { version = "49.0.0" }
+pyo3 = { version = "0.24.1", features = ["extension-module"] }
+tokio = { version = "1.46.1", features = ["rt"] }
+
+
+[build-dependencies]
+pyo3-build-config = "0.24"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+[lib]
+name = "xarray_sql"
+crate-type = ["cdylib", "rlib"]
diff --git a/pyproject.toml b/pyproject.toml
index 8766a925..216458a8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,3 +1,7 @@
+[build-system]
+requires = ["maturin>=1.9,<2.0"]
+build-backend = "maturin"
+
 [project]
 name = "xarray_sql"
 dynamic = ["version"]
@@ -22,11 +26,15 @@ classifiers = [
   "Programming Language :: Python :: 3.11",
   "Programming Language :: Python :: 3.12",
   "Programming Language :: Python :: 3.13",
+  "Programming Language :: Rust",
+  "Programming Language :: Python :: Implementation :: CPython",
+  "Programming Language :: Python :: Implementation :: PyPy",
   "Topic :: Scientific/Engineering :: Atmospheric Science",
   "Topic :: Database :: Front-Ends",
 ]
 dependencies = [
-    "datafusion>=47.0.0",
+    "dask>=2024.8.0",
+    "datafusion==49.0.0",  # This needs to match the cargo datafusion version!!
     "xarray>=2024.7.0",
 ]
 
@@ -35,16 +43,15 @@ test = [
   "pytest",
   "xarray[io]",
   "gcsfs",
-  "dask>=2024.8.0",
 ]
 
 [project.urls]
 Homepage = "https://github.com/alxmrs/xarray-sql"
 Issues = "https://github.com/alxmrs/xarray-sql/issues"
 
-[build-system]
-requires = ["setuptools>=64", "setuptools_scm>=8"]
-build-backend = "setuptools.build_meta"
+[tool.maturin]
+features = ["pyo3/extension-module"]
+module-name = "xarray_sql._native"
 
 [tool.setuptools.packages.find]
 exclude = ["demo", "perf_tests"]
@@ -83,7 +90,5 @@ dev = [
     "xarray_sql[test]",
     "py-spy>=0.4.0",
     "pyink>=24.10.1",
-    "mypy>=1.11.0",
-    "pre-commit>=3.0.0",
-    "pandas-stubs>=2.2.2.240807",
+    "maturin>=1.9.1",
 ]
diff --git a/src/lib.rs b/src/lib.rs
new file mode 100644
index 00000000..693b1281
--- /dev/null
+++ b/src/lib.rs
@@ -0,0 +1,244 @@
+// Licensed under the Apache License, Version 2.0
+//!
+//! Lazy Arrow stream table provider for xarray-sql.
+//!
+//! This module provides `LazyArrowStreamTable`, which wraps a Python object
+//! implementing `__arrow_c_stream__` and exposes it as a DataFusion TableProvider
+//! via the `__datafusion_table_provider__` protocol.
+//!
+//! The key feature is **lazy evaluation**: data is not read from the Python stream
+//! until query execution time (during `collect()`), not at registration time.
+
+use std::ffi::c_void;
+use std::ffi::CString;
+use std::fmt::Debug;
+use std::sync::Arc;
+
+use arrow::array::RecordBatch;
+use arrow::datatypes::SchemaRef;
+use arrow::ffi_stream::ArrowArrayStreamReader;
+use arrow::pyarrow::FromPyArrow;
+use datafusion::catalog::streaming::StreamingTable;
+use datafusion::datasource::TableProvider;
+use datafusion::execution::TaskContext;
+use datafusion::physical_plan::memory::MemoryStream;
+use datafusion::physical_plan::streaming::PartitionStream;
+use datafusion::physical_plan::SendableRecordBatchStream;
+use datafusion_ffi::table_provider::FFI_TableProvider;
+use pyo3::prelude::*;
+use pyo3::types::PyCapsule;
+use tokio::runtime::Handle;
+
+/// A partition stream that wraps a Python factory function that creates streams.
+///
+/// The factory is called lazily on each `execute()` invocation, allowing
+/// the same table to be queried multiple times.
+struct PyArrowStreamPartition {
+    schema: SchemaRef,
+    /// A Python callable (factory) that returns a fresh stream implementing `__arrow_c_stream__`.
+    /// Called on each execute() to create a new stream.
+    stream_factory: Py<PyAny>,
+}
+
+impl PyArrowStreamPartition {
+    fn new(stream_factory: Py<PyAny>, schema: SchemaRef) -> Self {
+        Self {
+            schema,
+            stream_factory,
+        }
+    }
+}
+
+impl Debug for PyArrowStreamPartition {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        f.debug_struct("PyArrowStreamPartition")
+            .field("schema", &self.schema)
+            .finish()
+    }
+}
+
+impl PartitionStream for PyArrowStreamPartition {
+    fn schema(&self) -> &SchemaRef {
+        &self.schema
+    }
+
+    fn execute(&self, _ctx: Arc<TaskContext>) -> SendableRecordBatchStream {
+        // Call the factory to get a fresh stream for this execution
+        let batches: Vec<RecordBatch> = Python::with_gil(|py| {
+            // Call the factory to get a fresh stream
+            let stream_result = self.stream_factory.call0(py);
+
+            match stream_result {
+                Ok(stream_obj) => {
+                    let bound = stream_obj.bind(py);
+
+                    match ArrowArrayStreamReader::from_pyarrow_bound(bound) {
+                        Ok(reader) => {
+                            // Collect batches, propagating errors as warnings
+                            // In streaming context, we can't easily return errors,
+                            // so we log and skip failed batches
+                            reader
+                                .filter_map(|result| match result {
+                                    Ok(batch) => Some(batch),
+                                    Err(e) => {
+                                        eprintln!("Warning: Failed to read batch: {e}");
+                                        None
+                                    }
+                                })
+                                .collect()
+                        }
+                        Err(e) => {
+                            eprintln!("Warning: Failed to create stream reader: {e}");
+                            vec![]
+                        }
+                    }
+                }
+                Err(e) => {
+                    eprintln!("Warning: Failed to call stream factory: {e}");
+                    vec![]
+                }
+            }
+        });
+
+        Box::pin(
+            MemoryStream::try_new(batches, Arc::clone(&self.schema), None)
+                .expect("MemoryStream creation should not fail with valid schema"),
+        )
+    }
+}
+
+/// A lazy table provider that wraps a Python stream factory.
+///
+/// This class implements the `__datafusion_table_provider__` protocol, allowing
+/// it to be registered with DataFusion's `SessionContext.register_table()`.
+///
+/// Data is NOT read until query execution time - this enables true lazy evaluation.
+/// The factory function is called on each query execution to create a fresh stream,
+/// allowing the same table to be queried multiple times.
+///
+/// # Example
+///
+/// ```python
+/// from datafusion import SessionContext
+/// from xarray_sql import LazyArrowStreamTable, XarrayRecordBatchReader
+///
+/// # Create a factory that produces lazy readers
+/// def make_reader():
+///     return XarrayRecordBatchReader(ds, chunks={'time': 240})
+///
+/// # Get schema from a sample reader
+/// sample = make_reader()
+/// schema = sample.schema
+///
+/// # Wrap factory in lazy table - NO DATA LOADED
+/// table = LazyArrowStreamTable(make_reader, schema)
+///
+/// # Register with DataFusion - STILL NO DATA LOADED
+/// ctx = SessionContext()
+/// ctx.register_table("air", table)
+///
+/// # Data only loaded HERE during collect()
+/// # Each query creates a fresh stream via the factory
+/// result = ctx.sql("SELECT AVG(air) FROM air").collect()
+/// result2 = ctx.sql("SELECT * FROM air LIMIT 10").collect()  # Works!
+/// ```
+#[pyclass(name = "LazyArrowStreamTable")]
+struct LazyArrowStreamTable {
+    /// The underlying StreamingTable
+    table: Arc<StreamingTable>,
+}
+
+#[pymethods]
+impl LazyArrowStreamTable {
+    /// Create a new LazyArrowStreamTable from a stream factory function.
+    ///
+    /// Args:
+    ///     stream_factory: A callable that returns a Python object implementing
+    ///             the Arrow PyCapsule interface (`__arrow_c_stream__`).
+    ///             Called on each query execution to create a fresh stream.
+    ///     schema: A PyArrow Schema for the table. Required since the factory
+    ///             hasn't been called yet.
+    ///
+    /// Raises:
+    ///     TypeError: If the schema is not a valid PyArrow Schema.
+    #[new]
+    fn new(stream_factory: &Bound<'_, PyAny>, schema: &Bound<'_, PyAny>) -> PyResult<Self> {
+        // Convert the PyArrow schema to Arrow schema
+        use arrow::datatypes::Schema;
+        use arrow::pyarrow::FromPyArrow;
+
+        let arrow_schema = Schema::from_pyarrow_bound(schema).map_err(|e| {
+            pyo3::exceptions::PyTypeError::new_err(format!("Failed to convert schema: {e}"))
+        })?;
+        let schema_ref = Arc::new(arrow_schema);
+
+        // Create the partition stream with the factory
+        let partition =
+            PyArrowStreamPartition::new(stream_factory.clone().unbind(), schema_ref.clone());
+
+        // Create the StreamingTable
+        let table =
+            StreamingTable::try_new(schema_ref, vec![Arc::new(partition)]).map_err(|e| {
+                pyo3::exceptions::PyRuntimeError::new_err(format!(
+                    "Failed to create StreamingTable: {e}"
+                ))
+            })?;
+
+        Ok(Self {
+            table: Arc::new(table),
+        })
+    }
+
+    /// Returns a PyCapsule implementing the DataFusion TableProvider FFI.
+    ///
+    /// This method is called by DataFusion's `register_table()` to get a
+    /// foreign table provider that can be used in queries.
+    fn __datafusion_table_provider__<'py>(&self, py: Python<'py>) -> PyResult<Bound<'py, PyCapsule>> {
+        // Create the FFI table provider
+        let provider: Arc<dyn TableProvider + Send> = self.table.clone();
+
+        // Try to get the current tokio runtime handle (available when called from DataFusion context)
+        let runtime = Handle::try_current().ok();
+
+        // Create FFI wrapper (v49 API takes 3 arguments)
+        let ffi_provider = FFI_TableProvider::new(
+            provider,
+            false, // can_support_pushdown_filters
+            runtime,
+        );
+
+        // Create the capsule name
+        let name = CString::new("datafusion_table_provider").unwrap();
+
+        // Create the PyCapsule with a destructor closure
+        // The PyCapsule takes ownership of the FFI_TableProvider
+        PyCapsule::new_with_destructor(
+            py,
+            ffi_provider,
+            Some(name),
+            |_provider: FFI_TableProvider, _context: *mut c_void| {
+                // The FFI_TableProvider will be dropped automatically
+            },
+        )
+    }
+
+    /// Get the schema of the table as a PyArrow Schema.
+    fn schema(&self, py: Python<'_>) -> PyResult<PyObject> {
+        use arrow::pyarrow::ToPyArrow;
+        self.table.schema().to_pyarrow(py)
+    }
+
+    fn __repr__(&self) -> String {
+        format!(
+            "LazyArrowStreamTable(schema={:?})",
+            self.table.schema()
+        )
+    }
+}
+
+/// Python module initialization
+#[pymodule]
+fn _native(m: &Bound<'_, PyModule>) -> PyResult<()> {
+    m.add_class::<LazyArrowStreamTable>()?;
+    Ok(())
+}
diff --git a/uv.lock b/uv.lock
index 1369363c..a98c650e 100644
--- a/uv.lock
+++ b/uv.lock
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 name = "webob"
 version = "1.8.9"
@@ -2804,6 +2594,8 @@ io = [
 name = "xarray-sql"
 source = { editable = "." }
 dependencies = [
+    { name = "dask", version = "2024.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "dask", version = "2025.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
     { name = "datafusion" },
     { name = "xarray", version = "2024.7.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
     { name = "xarray", version = "2025.6.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
@@ -2812,8 +2604,6 @@ dependencies = [
 
 [package.optional-dependencies]
 test = [
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-    { name = "dask", version = "2025.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
     { name = "gcsfs" },
     { name = "pytest" },
     { name = "xarray", version = "2024.7.0", source = { registry = "https://pypi.org/simple" }, extra = ["io"], marker = "python_full_version < '3.10'" },
@@ -2823,11 +2613,7 @@ test = [
 
 [package.dev-dependencies]
 dev = [
-    { name = "mypy" },
-    { name = "pandas-stubs", version = "2.2.2.240807", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
-    { name = "pandas-stubs", version = "2.3.2.250926", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
-    { name = "pre-commit", version = "4.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
-    { name = "pre-commit", version = "4.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+    { name = "maturin" },
     { name = "py-spy" },
     { name = "pyink" },
     { name = "xarray-sql", extra = ["test"] },
@@ -2835,8 +2621,8 @@ dev = [
 
 [package.metadata]
 requires-dist = [
-    { name = "dask", marker = "extra == 'test'", specifier = ">=2024.8.0" },
-    { name = "datafusion", specifier = ">=47.0.0" },
+    { name = "dask", specifier = ">=2024.8.0" },
+    { name = "datafusion", specifier = "==49.0.0" },
     { name = "gcsfs", marker = "extra == 'test'" },
     { name = "pytest", marker = "extra == 'test'" },
     { name = "xarray", specifier = ">=2024.7.0" },
@@ -2846,9 +2632,7 @@ provides-extras = ["test"]
 
 [package.metadata.requires-dev]
 dev = [
-    { name = "mypy", specifier = ">=1.11.0" },
-    { name = "pandas-stubs", specifier = ">=2.2.2.240807" },
-    { name = "pre-commit", specifier = ">=3.0.0" },
+    { name = "maturin", specifier = ">=1.9.1" },
     { name = "py-spy", specifier = ">=0.4.0" },
     { name = "pyink", specifier = ">=24.10.1" },
     { name = "xarray-sql", extras = ["test"] },
diff --git a/xarray_sql/__init__.py b/xarray_sql/__init__.py
index 6c55f5a5..3846fe22 100644
--- a/xarray_sql/__init__.py
+++ b/xarray_sql/__init__.py
@@ -1,2 +1,16 @@
 from .df import read_xarray, from_map
+from .reader import XarrayRecordBatchReader, read_xarray_lazy, read_xarray_table
 from .sql import XarrayContext
+from ._native import LazyArrowStreamTable
+
+__all__ = [
+    # High-level API (recommended)
+    "read_xarray_table",
+    "XarrayContext",
+    # Lower-level building blocks
+    "read_xarray",
+    "read_xarray_lazy",
+    "from_map",
+    "XarrayRecordBatchReader",
+    "LazyArrowStreamTable",
+]
diff --git a/xarray_sql/reader.py b/xarray_sql/reader.py
new file mode 100644
index 00000000..74a5db2e
--- /dev/null
+++ b/xarray_sql/reader.py
@@ -0,0 +1,237 @@
+"""Lazy Arrow stream reader for xarray Datasets.
+
+This module provides XarrayRecordBatchReader, which implements the Arrow
+PyCapsule Interface (__arrow_c_stream__) to enable zero-copy, lazy streaming
+of xarray data to DataFusion and other Arrow consumers.
+
+The implementation delegates to PyArrow's RecordBatchReader for the
+actual stream implementation, wrapping xarray block iteration in a generator.
+"""
+
+from __future__ import annotations
+
+import typing as t
+
+import pyarrow as pa
+import xarray as xr
+
+from .df import Block, Chunks, block_slices, pivot, _parse_schema
+
+if t.TYPE_CHECKING:
+  from ._native import LazyArrowStreamTable
+
+
+class XarrayRecordBatchReader:
+  """A lazy Arrow stream reader for xarray Datasets.
+
+  Implements the Arrow PyCapsule Interface (__arrow_c_stream__) to enable
+  zero-copy, lazy streaming of xarray data to DataFusion and other Arrow
+  consumers.
+
+  The key property is that xarray blocks are only converted to Arrow
+  RecordBatches when the consumer calls get_next (e.g., during DataFusion's
+  collect()), NOT when the reader is created or registered.
+
+  Attributes:
+      schema: The Arrow schema for the stream.
+
+  Example:
+      >>> import xarray as xr
+      >>> from xarray_sql import XarrayRecordBatchReader
+      >>> ds = xr.tutorial.open_dataset('air_temperature')
+      >>> reader = XarrayRecordBatchReader(ds, chunks={'time': 240})
+      >>> # At this point, NO data has been read from xarray
+      >>> # Data is only read when consumed:
+      >>> import pyarrow as pa
+      >>> pa_reader = pa.RecordBatchReader.from_stream(reader)
+      >>> for batch in pa_reader:
+      ...     print(batch.num_rows)  # Data read here
+  """
+
+  def __init__(
+      self,
+      ds: xr.Dataset,
+      chunks: Chunks = None,
+      *,
+      _iteration_callback: t.Optional[t.Callable[[Block], None]] = None,
+  ):
+    """Initialize the lazy reader.
+
+    Args:
+        ds: An xarray Dataset. All data_vars must share the same dimensions.
+        chunks: Xarray-like chunks specification. If not provided, uses
+            the Dataset's existing chunks.
+        _iteration_callback: Internal callback for testing. Called with
+            each block dict just before it's converted to Arrow. This
+            allows tests to track when iteration actually occurs.
+    """
+    self._ds = ds
+    self._chunks = chunks
+    self._schema = _parse_schema(ds)
+    self._iteration_callback = _iteration_callback
+    self._consumed = False
+
+    # Validate dimensions
+    fst = next(iter(ds.values())).dims
+    if not all(da.dims == fst for da in ds.values()):
+      raise ValueError(
+          "All dimensions must be equal. Please filter data_vars in the Dataset."
+      )
+
+  @property
+  def schema(self) -> pa.Schema:
+    """The Arrow schema for this stream."""
+    return self._schema
+
+  def _generate_batches(self) -> t.Iterator[pa.RecordBatch]:
+    """Generate RecordBatches lazily from xarray blocks.
+
+    This generator is only consumed when the Arrow stream's get_next
+    is called, ensuring true lazy evaluation.
+    """
+    for block in block_slices(self._ds, self._chunks):
+      # Call the iteration callback if provided (for testing)
+      if self._iteration_callback is not None:
+        self._iteration_callback(block)
+
+      # Convert this block to a RecordBatch
+      df = pivot(self._ds.isel(block))
+      yield pa.RecordBatch.from_pandas(df, schema=self._schema)
+
+  def __arrow_c_stream__(
+      self, requested_schema: t.Optional[object] = None
+  ) -> object:
+    """Export as Arrow C Stream via PyCapsule.
+
+    This method is called by Arrow consumers (like DataFusion) to get
+    a C-level stream interface. The actual data iteration only begins
+    when the consumer calls get_next on the stream.
+
+    Args:
+        requested_schema: Optional schema for type casting. Currently
+            passed through to PyArrow's implementation.
+
+    Returns:
+        PyCapsule containing ArrowArrayStream pointer with name
+        "arrow_array_stream".
+
+    Raises:
+        RuntimeError: If the stream has already been consumed.
+    """
+    if self._consumed:
+      raise RuntimeError(
+          "Stream already consumed. XarrayRecordBatchReader can only "
+          "be iterated once. Create a new reader for additional iterations."
+      )
+    self._consumed = True
+
+    # Create a PyArrow RecordBatchReader from our generator
+    # The generator is NOT consumed here - only when get_next is called
+    reader = pa.RecordBatchReader.from_batches(
+        self._schema, self._generate_batches()
+    )
+
+    # Delegate to PyArrow's __arrow_c_stream__ implementation
+    return reader.__arrow_c_stream__(requested_schema)
+
+  def __arrow_c_schema__(
+      self, requested_schema: t.Optional[object] = None
+  ) -> object:
+    """Export the schema as Arrow C Schema via PyCapsule.
+
+    This allows consumers to inspect the schema without consuming the stream.
+
+    Args:
+        requested_schema: Optional schema for negotiation (unused).
+
+    Returns:
+        PyCapsule containing ArrowSchema pointer.
+    """
+    return self._schema.__arrow_c_schema__()
+
+
+def read_xarray_lazy(
+    ds: xr.Dataset, chunks: Chunks = None
+) -> XarrayRecordBatchReader:
+  """Create a lazy Arrow stream reader from an xarray Dataset.
+
+  This is the recommended way to register xarray data with DataFusion
+  for lazy evaluation. Data is only read when queries are executed
+  (e.g., during collect()), not during registration.
+
+  Args:
+      ds: An xarray Dataset. All data_vars must share the same dimensions.
+      chunks: Xarray-like chunks specification. If not provided, uses
+          the Dataset's existing chunks.
+
+  Returns:
+      An XarrayRecordBatchReader that implements __arrow_c_stream__.
+
+  Example:
+      >>> from datafusion import SessionContext
+      >>> import xarray as xr
+      >>> from xarray_sql import read_xarray_lazy, LazyArrowStreamTable
+      >>>
+      >>> ds = xr.tutorial.open_dataset('air_temperature')
+      >>> reader = read_xarray_lazy(ds, chunks={'time': 240})
+      >>> table = LazyArrowStreamTable(reader)
+      >>>
+      >>> ctx = SessionContext()
+      >>> ctx.register_table_provider('air', table)
+      >>>
+      >>> # Data is only read here, during collect()
+      >>> result = ctx.sql('SELECT AVG(air) FROM air').collect()
+  """
+  return XarrayRecordBatchReader(ds, chunks)
+
+
+def read_xarray_table(
+    ds: xr.Dataset,
+    chunks: Chunks = None,
+    *,
+    _iteration_callback: t.Optional[t.Callable[[Block], None]] = None,
+) -> "LazyArrowStreamTable":
+  """Create a lazy DataFusion table from an xarray Dataset.
+
+  This is the simplest way to register xarray data with DataFusion.
+  Data is only read when queries are executed (during collect()),
+  not during registration. The table can be queried multiple times.
+
+  Args:
+      ds: An xarray Dataset. All data_vars must share the same dimensions.
+      chunks: Xarray-like chunks specification. If not provided, uses
+          the Dataset's existing chunks.
+      _iteration_callback: Internal callback for testing. Called with
+          each block dict just before it's converted to Arrow.
+
+  Returns:
+      A LazyArrowStreamTable ready for registration with DataFusion.
+
+  Example:
+      >>> from datafusion import SessionContext
+      >>> import xarray as xr
+      >>> from xarray_sql import read_xarray_table
+      >>>
+      >>> ds = xr.tutorial.open_dataset('air_temperature')
+      >>> table = read_xarray_table(ds, chunks={'time': 240})
+      >>>
+      >>> ctx = SessionContext()
+      >>> ctx.register_table_provider('air', table)
+      >>>
+      >>> # Data is only read here, during collect()
+      >>> result = ctx.sql('SELECT AVG(air) FROM air').collect()
+      >>> # Can query again - each query creates a fresh stream
+      >>> result2 = ctx.sql('SELECT * FROM air LIMIT 10').collect()
+  """
+  from ._native import LazyArrowStreamTable
+
+  # Get schema from dataset without creating a stream
+  schema = _parse_schema(ds)
+
+  # Create a factory function that produces fresh streams on each call
+  def make_stream() -> XarrayRecordBatchReader:
+    return XarrayRecordBatchReader(
+        ds, chunks, _iteration_callback=_iteration_callback
+    )
+
+  return LazyArrowStreamTable(make_stream, schema)
diff --git a/xarray_sql/reader_test.py b/xarray_sql/reader_test.py
new file mode 100644
index 00000000..a00a15e6
--- /dev/null
+++ b/xarray_sql/reader_test.py
@@ -0,0 +1,453 @@
+"""Tests for XarrayRecordBatchReader lazy streaming behavior.
+
+These tests verify that XarrayRecordBatchReader provides true lazy evaluation:
+- No data iteration during reader creation
+- No data iteration during DataFusion table registration (using LazyArrowStreamTable)
+- Data iteration ONLY occurs during query execution (collect())
+
+The lazy streaming is achieved via the Rust LazyArrowStreamTable class which
+implements the __datafusion_table_provider__ protocol using StreamingTable.
+"""
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+import xarray as xr
+from datafusion import SessionContext
+
+from ._native import LazyArrowStreamTable
+from .reader import XarrayRecordBatchReader, read_xarray_lazy, read_xarray_table
+
+
+@pytest.fixture
+def small_ds():
+  """Create a small dataset for testing."""
+  np.random.seed(42)
+  time = pd.date_range("2020-01-01", periods=100, freq="h")
+  lat = np.linspace(-90, 90, 10)
+  lon = np.linspace(-180, 180, 10)
+
+  data = np.random.rand(100, 10, 10).astype(np.float32)
+
+  return xr.Dataset(
+      {"temperature": (["time", "lat", "lon"], data)},
+      coords={"time": time, "lat": lat, "lon": lon},
+  )
+
+
+class IterationTracker:
+  """Tracks when iteration occurs for testing lazy evaluation."""
+
+  def __init__(self):
+    self.iteration_count = 0
+    self.blocks_seen = []
+
+  def __call__(self, block):
+    self.iteration_count += 1
+    self.blocks_seen.append(block)
+
+  def reset(self):
+    self.iteration_count = 0
+    self.blocks_seen = []
+
+
+class TestXarrayRecordBatchReaderCreation:
+  """Tests that reader creation does NOT trigger data iteration."""
+
+  def test_reader_creation_does_not_iterate(self, small_ds):
+    """Creating a reader should NOT iterate through any data."""
+    tracker = IterationTracker()
+
+    reader = XarrayRecordBatchReader(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=tracker,
+    )
+
+    assert tracker.iteration_count == 0, (
+        f"Expected 0 iterations during reader creation, "
+        f"but got {tracker.iteration_count}"
+    )
+
+  def test_read_xarray_lazy_does_not_iterate(self, small_ds):
+    """read_xarray_lazy() should NOT iterate through any data."""
+    # We can't use the callback with the public API, but we can verify
+    # basic behavior - no exceptions and no data loaded
+    reader = read_xarray_lazy(small_ds, chunks={"time": 25})
+
+    assert reader is not None
+    assert reader.schema is not None
+    assert not reader._consumed
+
+  def test_schema_access_does_not_iterate(self, small_ds):
+    """Accessing the schema should NOT trigger iteration."""
+    tracker = IterationTracker()
+
+    reader = XarrayRecordBatchReader(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=tracker,
+    )
+
+    # Access schema
+    _ = reader.schema
+    _ = reader.__arrow_c_schema__()
+
+    assert tracker.iteration_count == 0, (
+        f"Expected 0 iterations when accessing schema, "
+        f"but got {tracker.iteration_count}"
+    )
+
+
+class TestDataFusionRegistration:
+  """Tests that DataFusion table registration does NOT trigger iteration.
+
+  These tests use read_xarray_table with register_table_provider()
+  to achieve true lazy evaluation.
+  """
+
+  def test_register_table_does_not_iterate(self, small_ds):
+    """Registering a LazyArrowStreamTable should NOT iterate data.
+
+    This is the KEY test for lazy evaluation. LazyArrowStreamTable wraps
+    a factory and implements __datafusion_table_provider__ with StreamingTable,
+    ensuring data is only read during query execution.
+    """
+    tracker = IterationTracker()
+
+    # Use read_xarray_table which creates a factory-based table
+    table = read_xarray_table(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=tracker,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table_provider("test_table", table)
+
+    assert tracker.iteration_count == 0, (
+        f"LAZY EVALUATION FAILED: Expected 0 iterations during "
+        f"register_table_provider(), but got {tracker.iteration_count}."
+    )
+
+  def test_lazy_table_creation_does_not_iterate(self, small_ds):
+    """Creating a LazyArrowStreamTable should NOT iterate data."""
+    tracker = IterationTracker()
+
+    # Use read_xarray_table which creates a factory-based table
+    table = read_xarray_table(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=tracker,
+    )
+
+    assert tracker.iteration_count == 0, (
+        f"LAZY EVALUATION FAILED: Expected 0 iterations during "
+        f"LazyArrowStreamTable creation, but got {tracker.iteration_count}."
+    )
+
+  def test_sql_planning_does_not_iterate(self, small_ds):
+    """Creating a SQL query plan should NOT iterate data."""
+    tracker = IterationTracker()
+
+    table = read_xarray_table(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=tracker,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table_provider("test_table", table)
+
+    # Create a query but don't execute it
+    query = ctx.sql("SELECT AVG(temperature) FROM test_table")
+
+    # Just creating the query shouldn't iterate
+    assert tracker.iteration_count == 0, (
+        f"Expected 0 iterations during SQL planning, "
+        f"but got {tracker.iteration_count}. "
+        f"DataFusion may be scanning data during query planning."
+    )
+
+
+class TestDataFusionCollect:
+  """Tests that data iteration ONLY occurs during collect().
+
+  These tests use read_xarray_table to verify lazy evaluation.
+  """
+
+  def test_collect_triggers_iteration(self, small_ds):
+    """collect() should trigger data iteration."""
+    tracker = IterationTracker()
+
+    table = read_xarray_table(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=tracker,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table_provider("test_table", table)
+
+    # Verify no iteration yet (lazy registration)
+    iteration_before_collect = tracker.iteration_count
+    assert (
+        iteration_before_collect == 0
+    ), "Should have 0 iterations before collect"
+
+    # Now collect - this SHOULD iterate
+    result = ctx.sql("SELECT * FROM test_table LIMIT 10").collect()
+
+    assert tracker.iteration_count > 0, (
+        f"Expected iterations during collect(), but got 0. "
+        f"Data was never read!"
+    )
+    assert (
+        tracker.iteration_count > iteration_before_collect
+    ), f"Expected more iterations after collect()"
+
+  def test_full_query_iterates_all_blocks(self, small_ds):
+    """A query that reads all data should iterate all blocks."""
+    tracker = IterationTracker()
+
+    chunks = {"time": 25}
+    table = read_xarray_table(
+        small_ds,
+        chunks=chunks,
+        _iteration_callback=tracker,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table_provider("test_table", table)
+
+    # Run a query that needs to scan all data
+    result = ctx.sql("SELECT COUNT(*) FROM test_table").collect()
+
+    # With time=100 and chunks=25, we expect 4 blocks
+    expected_blocks = 100 // 25
+    assert tracker.iteration_count == expected_blocks, (
+        f"Expected {expected_blocks} block iterations, "
+        f"but got {tracker.iteration_count}"
+    )
+
+  def test_aggregation_query_iterates_correctly(self, small_ds):
+    """Aggregation queries should iterate all necessary blocks."""
+    tracker = IterationTracker()
+
+    table = read_xarray_table(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=tracker,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table_provider("test_table", table)
+
+    # Run aggregation
+    result = ctx.sql(
+        "SELECT lat, AVG(temperature) as avg_temp "
+        "FROM test_table GROUP BY lat"
+    ).collect()
+
+    # Should have iterated some blocks
+    assert tracker.iteration_count > 0
+    assert len(result) > 0
+
+
+class TestLazyEvaluationEndToEnd:
+  """End-to-end tests verifying lazy evaluation through the full pipeline.
+
+  These tests use read_xarray_table to achieve true lazy evaluation.
+  """
+
+  def test_lazy_evaluation_sequence(self, small_ds):
+    """Verify the exact sequence of lazy evaluation stages.
+
+    This is the comprehensive test that proves true lazy evaluation:
+    1. Table creation: 0 iterations
+    2. Table registration: 0 iterations
+    3. Query planning: 0 iterations
+    4. collect(): N iterations (where N = number of blocks)
+    """
+    tracker = IterationTracker()
+
+    # Stage 1: Table creation (with factory)
+    table = read_xarray_table(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=tracker,
+    )
+    iterations_after_table = tracker.iteration_count
+    assert iterations_after_table == 0, (
+        f"Stage 1 FAILED: Table creation triggered "
+        f"{iterations_after_table} iterations"
+    )
+
+    # Stage 2: Table registration
+    ctx = SessionContext()
+    ctx.register_table_provider("test_table", table)
+    iterations_after_registration = tracker.iteration_count
+    assert iterations_after_registration == 0, (
+        f"Stage 2 FAILED: Table registration triggered "
+        f"{iterations_after_registration} iterations"
+    )
+
+    # Stage 3: Query planning
+    query = ctx.sql("SELECT * FROM test_table")
+    iterations_after_planning = tracker.iteration_count
+    assert iterations_after_planning == 0, (
+        f"Stage 3 FAILED: Query planning triggered "
+        f"{iterations_after_planning} iterations"
+    )
+
+    # Stage 4: collect() - NOW iteration should happen
+    result = query.collect()
+    iterations_after_collect = tracker.iteration_count
+    assert (
+        iterations_after_collect > 0
+    ), f"Stage 4 FAILED: collect() triggered 0 iterations - no data was read!"
+
+    # Verify we got the expected number of blocks (100 time steps / 25 = 4)
+    expected_blocks = 4
+    assert (
+        iterations_after_collect == expected_blocks
+    ), f"Expected {expected_blocks} iterations, got {iterations_after_collect}"
+
+  def test_multiple_queries_on_same_table(self, small_ds):
+    """Same table can be queried multiple times with fresh iteration each time."""
+    tracker = IterationTracker()
+
+    table = read_xarray_table(
+        small_ds,
+        chunks={"time": 50},
+        _iteration_callback=tracker,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table_provider("test_table", table)
+
+    # First query
+    ctx.sql("SELECT COUNT(*) FROM test_table").collect()
+    first_query_iterations = tracker.iteration_count
+    assert first_query_iterations > 0, "First query should iterate"
+
+    # Second query on same table - should iterate again
+    ctx.sql("SELECT AVG(temperature) FROM test_table").collect()
+    second_query_iterations = tracker.iteration_count
+    assert second_query_iterations > first_query_iterations, (
+        "Second query should trigger additional iterations"
+    )
+
+  def test_stream_consumed_error(self, small_ds):
+    """Once consumed, a single XarrayRecordBatchReader should not be reusable."""
+    reader = XarrayRecordBatchReader(small_ds, chunks={"time": 25})
+
+    # Consume the reader by converting to a PyArrow reader and reading
+    import pyarrow as pa
+    pa_reader = pa.RecordBatchReader.from_stream(reader)
+    _ = pa_reader.read_all()
+
+    # Reader is now consumed, calling __arrow_c_stream__ again should fail
+    with pytest.raises(RuntimeError, match="already consumed"):
+      reader.__arrow_c_stream__()
+
+
+class TestDataIntegrity:
+  """Tests that verify data correctness alongside lazy evaluation.
+
+  These tests use read_xarray_table for lazy streaming.
+  """
+
+  def test_query_results_are_correct(self, small_ds):
+    """Verify that lazy evaluation produces correct results."""
+    table = read_xarray_table(small_ds, chunks={"time": 25})
+
+    ctx = SessionContext()
+    ctx.register_table_provider("test_table", table)
+
+    # Get count
+    result = ctx.sql("SELECT COUNT(*) as cnt FROM test_table").collect()
+    count = result[0].to_pandas()["cnt"].iloc[0]
+
+    # Expected: 100 time steps * 10 lat * 10 lon = 10,000 rows
+    expected_count = 100 * 10 * 10
+    assert (
+        count == expected_count
+    ), f"Expected {expected_count} rows, got {count}"
+
+  def test_aggregation_results_are_correct(self, small_ds):
+    """Verify aggregation produces correct results."""
+    table = read_xarray_table(small_ds, chunks={"time": 25})
+
+    ctx = SessionContext()
+    ctx.register_table_provider("test_table", table)
+
+    # Get average temperature
+    result = ctx.sql(
+        "SELECT AVG(temperature) as avg_temp FROM test_table"
+    ).collect()
+    avg_temp = result[0].to_pandas()["avg_temp"].iloc[0]
+
+    # With seed 42 and random data in [0, 1), average should be ~0.5
+    assert (
+        0.4 < avg_temp < 0.6
+    ), f"Expected average temperature ~0.5, got {avg_temp}"
+
+
+class TestPyArrowInterop:
+  """Tests for PyArrow interoperability."""
+
+  def test_from_stream_does_not_iterate(self, small_ds):
+    """pa.RecordBatchReader.from_stream() should not iterate."""
+    tracker = IterationTracker()
+
+    reader = XarrayRecordBatchReader(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=tracker,
+    )
+
+    # Create PyArrow reader from our stream
+    pa_reader = pa.RecordBatchReader.from_stream(reader)
+
+    assert tracker.iteration_count == 0, (
+        f"Expected 0 iterations when creating PyArrow reader, "
+        f"but got {tracker.iteration_count}"
+    )
+
+  def test_pyarrow_iteration_triggers_callbacks(self, small_ds):
+    """Iterating via PyArrow should trigger our callbacks."""
+    tracker = IterationTracker()
+
+    reader = XarrayRecordBatchReader(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=tracker,
+    )
+
+    pa_reader = pa.RecordBatchReader.from_stream(reader)
+
+    # Now iterate
+    for batch in pa_reader:
+      pass
+
+    assert (
+        tracker.iteration_count == 4
+    ), f"Expected 4 iterations, got {tracker.iteration_count}"
+
+  def test_read_all_iterates_all(self, small_ds):
+    """read_all() should iterate through all blocks."""
+    tracker = IterationTracker()
+
+    reader = XarrayRecordBatchReader(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=tracker,
+    )
+
+    pa_reader = pa.RecordBatchReader.from_stream(reader)
+    table = pa_reader.read_all()
+
+    assert tracker.iteration_count == 4
+    assert len(table) == 100 * 10 * 10
diff --git a/xarray_sql/sql.py b/xarray_sql/sql.py
index 11553b99..851fd0c5 100644
--- a/xarray_sql/sql.py
+++ b/xarray_sql/sql.py
@@ -1,7 +1,8 @@
 import xarray as xr
 from datafusion import SessionContext
 
-from .df import read_xarray, Chunks
+from .df import Chunks
+from .reader import read_xarray_table
 
 
 class XarrayContext(SessionContext):
@@ -13,5 +14,5 @@ def from_dataset(
       input_table: xr.Dataset,
       chunks: Chunks = None,
   ):
-    arrow_table = read_xarray(input_table, chunks)
-    return self.from_arrow(arrow_table, table_name)
+    table = read_xarray_table(input_table, chunks)
+    self.register_table_provider(table_name, table)