diff --git a/.github/workflows/ci-build.yml b/.github/workflows/ci-build.yml
new file mode 100644
index 0000000..214388e
--- /dev/null
+++ b/.github/workflows/ci-build.yml
@@ -0,0 +1,58 @@
+name: ci-build
+
+on:
+  # Triggers the workflow on push or pull request events but only for the main branch
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  build:
+    name: "python ${{ matrix.platform.runner }}-${{ matrix.platform.target }} build"
+    runs-on: ${{ matrix.platform.runner }}
+    strategy:
+      fail-fast: false
+      matrix:
+        platform:
+          - runner: windows-latest
+            target: x86
+            python_arch: x86
+          - runner: macos-latest
+            target: aarch64
+          - runner: ubuntu-latest
+            target: aarch64
+          - runner: ubuntu-latest
+            target: x86_64
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: dtolnay/rust-toolchain@stable
+
+      - name: Setup sccache
+        uses: mozilla-actions/sccache-action@v0.0.9
+
+      - name: Configure sccache
+        run: |
+          echo "SCCACHE_GHA_ENABLED=true" >> $GITHUB_ENV
+          echo "RUSTC_WRAPPER=sccache" >> $GITHUB_ENV
+
+      - name: Cache Cargo
+        uses: actions/cache@v4
+        with:
+          path: ~/.cargo
+          key: cargo-cache-${{ steps.rust-toolchain.outputs.cachekey }}-${{ matrix.platform.runner }}-${{ matrix.platform.target }}-${{ hashFiles('Cargo.lock') }}
+
+      - uses: astral-sh/setup-uv@v5
+        with:
+          python-version: 3.11
+          enable-cache: true
+
+      # Use the --no-install-package to only install the dependencies
+      # but do not yet build the rust library
+      - name: The project builds
+        run: |
+          uv sync --dev --no-install-package xarray-sql
+          uv run --no-project maturin build
diff --git a/.github/workflows/ci-rust.yml b/.github/workflows/ci-rust.yml
new file mode 100644
index 0000000..68f1ce6
--- /dev/null
+++ b/.github/workflows/ci-rust.yml
@@ -0,0 +1,59 @@
+name: ci-rust
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  lint-test-all-features:
+    name: Lint and Test
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: "recursive"
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+
+      - name: Install Rust
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          components: clippy
+
+      - name: Setup sccache
+        uses: mozilla-actions/sccache-action@v0.0.9
+
+      - name: Configure sccache
+        run: |
+          echo "SCCACHE_GHA_ENABLED=true" >> $GITHUB_ENV
+          echo "RUSTC_WRAPPER=sccache" >> $GITHUB_ENV
+
+      - name: Clippy
+        run: cargo clippy --all-features --tests -- -D warnings
+
+      - name: Check
+        run: cargo check --all-features
+
+      - name: Test
+        run: cargo test --all-features
+
+  fmt:
+    name: Fmt
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: "recursive"
+      - name: Install Rust
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          components: rustfmt
+      - name: Fmt
+        run: cargo fmt -- --check --config imports_granularity=Module,group_imports=StdExternalCrate
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 4e66845..c1d892d 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -33,7 +33,6 @@ jobs:
       fail-fast: false
       matrix:
         python-version: [
-          "3.9",
           "3.10",
           "3.11",
           "3.12",
@@ -41,11 +40,33 @@ jobs:
         ]
     steps:
       - uses: actions/checkout@v4
-      - name: Install uv and set the python version
-        uses: astral-sh/setup-uv@v5
+
+      - uses: dtolnay/rust-toolchain@stable
+
+      - name: Setup sccache
+        uses: mozilla-actions/sccache-action@v0.0.9
+
+      - name: Configure sccache
+        run: |
+          echo "SCCACHE_GHA_ENABLED=true" >> $GITHUB_ENV
+          echo "RUSTC_WRAPPER=sccache" >> $GITHUB_ENV
+
+      - name: Cache Cargo
+        uses: actions/cache@v4
+        with:
+          path: ~/.cargo
+          key: cargo-cache-${{ steps.rust-toolchain.outputs.cachekey }}-${{ hashFiles('Cargo.lock') }}
+
+      - uses: astral-sh/setup-uv@v5
         with:
           python-version: ${{ matrix.python-version }}
+          enable-cache: true
+
+        # Use the --no-install-package to only install the dependencies
+        # but do not yet build the rust library
       - name: Install xarray_sql
-        run: uv sync --dev
+        run: uv sync --dev --no-install-package xarray-sql
+      - name: build rust
+        run: uv run --no-project maturin develop --uv
       - name: Run unit tests
-        run: uv run pytest -m "not integration"
+        run: uv run --no-project pytest -v . -m "not integration"
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index 9d83a02..a061ad1 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -1,4 +1,5 @@
-# Taken from Xee and minimally modified: https://github.com/google/Xee/blob/main/.github/workflows/publish.yml
+# Taken from Xee and modified: https://github.com/google/Xee/blob/main/.github/workflows/publish.yml
+# Rust/maturin release adapted from https://github.com/apache/datafusion-python/blob/main/.github/workflows/build.yml
 #
 # Copyright 2023 Google LLC
 #
@@ -13,6 +14,27 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+#
+#
+# Copyright 2022 Apache Software Foundation
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
 name: Publish to PyPi
 
 on:
@@ -20,78 +42,152 @@ on:
     types: [published]
 
 jobs:
-  build-artifacts:
-    runs-on: ubuntu-latest
+  build-artifacts-mac-win:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ macos-latest, macos-15-intel, windows-latest ]
     steps:
       - uses: actions/checkout@v4
-      - name: Set up Python
-        uses: actions/setup-python@v2.3.1
-        with:
-          python-version: 3.11
 
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install setuptools setuptools-scm wheel twine check-manifest
+      - uses: dtolnay/rust-toolchain@stable
+
+      - name: Setup sccache
+        uses: mozilla-actions/sccache-action@v0.0.9
 
-      - name: Build tarball and wheels
+      - name: Configure sccache
         run: |
-          git clean -xdf
-          git restore -SW .
-          python -m build --sdist --wheel .
-      - name: Check built artifacts
+          echo "SCCACHE_GHA_ENABLED=true" >> $GITHUB_ENV
+          echo "RUSTC_WRAPPER=sccache" >> $GITHUB_ENV
+
+      - uses: astral-sh/setup-uv@v7
+        with:
+          enable-cache: true
+
+      - name: Build wheels
         run: |
-          python -m twine check dist/*
-          pwd
+          uv sync --dev --no-install-package xarray-sql
+          uv run --no-project maturin build --release --strip
+
       - uses: actions/upload-artifact@v4
         with:
-          name: releases
-          path: dist
+          name: dist-${{ matrix.os }}
+          path: target/wheels/*
 
-  test-built-dist:
-    needs: build-artifacts
+  build-artifacts-manylinux-x86_64:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/setup-python@v2.3.1
-        name: Install Python
+      - uses: actions/checkout@v4
+
+      - name: Build wheels for manylinux x86_64
+        uses: PyO3/maturin-action@v1
+        env:
+          RUST_BACKTRACE: 1
+        with:
+          rust-toolchain: stable
+          target: x86_64
+          manylinux: 2014
+          rustup-components: rust-std rustfmt
+          sccache: 'true'
+          args: --release
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: dist-manylinux-x86_64
+          path: target/wheels/*
+
+  build-artifacts-manylinux-arm64:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Build wheels for manylinux arm64
+        uses: PyO3/maturin-action@v1
+        env:
+          RUST_BACKTRACE: 1
         with:
-          python-version: 3.11
+          rust-toolchain: stable
+          target: aarch64
+          # Use manylinux_2_28-cross because the manylinux2014-cross has GCC 4.8.5, which causes the build to fail
+          manylinux: 2_28
+          rustup-components: rust-std rustfmt
+          sccache: 'true'
+          args: --release
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: dist-manylinux-aarch64
+          path: target/wheels/*
+
+  build-sdist:
+    name: Source distribution
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Build sdist
+        uses: PyO3/maturin-action@v1
+        with:
+          rust-toolchain: stable
+          manylinux: auto
+          rustup-components: rust-std rustfmt
+          args: --release --sdist --out dist
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: dist-sdist
+          path: dist/*
+
+  merge-build-artifacts:
+    runs-on: ubuntu-latest
+    needs:
+      - build-artifacts-mac-win
+      - build-artifacts-manylinux-x86_64
+      - build-artifacts-manylinux-arm64
+      - build-sdist
+    steps:
+      - name: Merge Build Artifacts
+        uses: actions/upload-artifact/merge@v4
+        with:
+          name: dist
+          pattern: dist-*
+
+  verify-built-dist:
+    needs: merge-build-artifacts
+    runs-on: ubuntu-latest
+    steps:
       - uses: actions/download-artifact@v4
         with:
-          name: releases
+          name: dist
           path: dist
+
       - name: List contents of built dist
         run: |
           ls -ltrh
           ls -ltrh dist
       - name: Publish package to TestPyPI
         if: github.event_name == 'push'
-        uses: pypa/gh-action-pypi-publish@v1.13.0
-        with:
-          user: __token__
-          password: ${{ secrets.TESTPYPI_TOKEN }}
-          repository_url: https://test.pypi.org/legacy/
-          verbose: true
+        run: uv publish --token ${{ secrets.TESTPYPI_TOKEN }} --username __token__
 
       - name: Check uploaded package
         if: github.event_name == 'push'
         run: |
           sleep 3
-          python -m pip install --upgrade pip
-          python -m pip install --extra-index-url https://test.pypi.org/simple --upgrade de
+          uv pip install --extra-index-url https://test.pypi.org/simple --upgrade de
           python -c "import xarray_sql; print(xarray_sql.__version__)"
   upload-to-pypi:
-    needs: test-built-dist
-    if: github.event_name == 'release'
+    needs: verify-built-dist
     runs-on: ubuntu-latest
     steps:
       - uses: actions/download-artifact@v4
         with:
-          name: releases
+          name: dist
           path: dist
-      - name: Publish package to PyPI
-        uses: pypa/gh-action-pypi-publish@v1.13.0
+
+      - uses: astral-sh/setup-uv@v7
         with:
-          user: __token__
-          password: ${{ secrets.PYPI_TOKEN }}
-          verbose: true
+          enable-cache: true
+
+      - name: Publish package to PyPI
+        run: uv publish --token ${{ secrets.PYPI_TOKEN }} --username __token__
diff --git a/.gitignore b/.gitignore
index 1f7411d..bc7aa06 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,3 +6,6 @@ __pycache__
 .pytest_cache
 .venv
 .idea
+target
+test_data
+*.so
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 462af82..73875b9 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -21,3 +21,13 @@ repos:
       - id: mypy
         additional_dependencies: [types-setuptools]
         args: [--ignore-missing-imports]
+
+  - repo: local
+    hooks:
+      - id: rustfmt
+        name: rustfmt
+        description: Check if all files follow the rustfmt style.
+        entry: cargo fmt -- --config imports_granularity=Module,group_imports=StdExternalCrate
+        language: system
+        types: [ file, rust ]
+        pass_filenames: false
diff --git a/Cargo.lock b/Cargo.lock
new file mode 100644
index 0000000..63e8b27
--- /dev/null
+++ b/Cargo.lock
@@ -0,0 +1,3570 @@
+# This file is automatically @generated by Cargo.
+# It is not intended for manual editing.
+version = 4
+
+[[package]]
+name = "abi_stable"
+version = "0.11.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "69d6512d3eb05ffe5004c59c206de7f99c34951504056ce23fc953842f12c445"
+dependencies = [
+ "abi_stable_derive",
+ "abi_stable_shared",
+ "const_panic",
+ "core_extensions",
+ "crossbeam-channel",
+ "generational-arena",
+ "libloading",
+ "lock_api",
+ "parking_lot",
+ "paste",
+ "repr_offset",
+ "rustc_version",
+ "serde",
+ "serde_derive",
+ "serde_json",
+]
+
+[[package]]
+name = "abi_stable_derive"
+version = "0.11.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d7178468b407a4ee10e881bc7a328a65e739f0863615cca4429d43916b05e898"
+dependencies = [
+ "abi_stable_shared",
+ "as_derive_utils",
+ "core_extensions",
+ "proc-macro2",
+ "quote",
+ "rustc_version",
+ "syn 1.0.109",
+ "typed-arena",
+]
+
+[[package]]
+name = "abi_stable_shared"
+version = "0.11.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b2b5df7688c123e63f4d4d649cba63f2967ba7f7861b1664fca3f77d3dad2b63"
+dependencies = [
+ "core_extensions",
+]
+
+[[package]]
+name = "adler2"
+version = "2.0.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "320119579fcad9c21884f5c4861d16174d0e06250625266f50fe6898340abefa"
+
+[[package]]
+name = "ahash"
+version = "0.8.12"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5a15f179cd60c4584b8a8c596927aadc462e27f2ca70c04e0071964a73ba7a75"
+dependencies = [
+ "cfg-if",
+ "const-random",
+ "getrandom 0.3.3",
+ "once_cell",
+ "version_check",
+ "zerocopy",
+]
+
+[[package]]
+name = "aho-corasick"
+version = "1.1.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8e60d3430d3a69478ad0993f19238d2df97c507009a52b3c10addcd7f6bcb916"
+dependencies = [
+ "memchr",
+]
+
+[[package]]
+name = "alloc-no-stdlib"
+version = "2.0.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "cc7bb162ec39d46ab1ca8c77bf72e890535becd1751bb45f64c597edb4c8c6b3"
+
+[[package]]
+name = "alloc-stdlib"
+version = "0.2.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "94fb8275041c72129eb51b7d0322c29b8387a0386127718b096429201a5d6ece"
+dependencies = [
+ "alloc-no-stdlib",
+]
+
+[[package]]
+name = "allocator-api2"
+version = "0.2.21"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "683d7910e743518b0e34f1186f92494becacb047c7b6bf616c96772180fef923"
+
+[[package]]
+name = "android_system_properties"
+version = "0.1.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "819e7219dbd41043ac279b19830f2efc897156490d7fd6ea916720117ee66311"
+dependencies = [
+ "libc",
+]
+
+[[package]]
+name = "anyhow"
+version = "1.0.100"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a23eb6b1614318a8071c9b2521f36b424b2c83db5eb3a0fead4a6c0809af6e61"
+
+[[package]]
+name = "arrayref"
+version = "0.3.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "76a2e8124351fda1ef8aaaa3bbd7ebbcb486bbcd4225aca0aa0d84bb2db8fecb"
+
+[[package]]
+name = "arrayvec"
+version = "0.7.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7c02d123df017efcdfbd739ef81735b36c5ba83ec3c59c80a9d7ecc718f92e50"
+
+[[package]]
+name = "arrow"
+version = "57.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2a2b10dcb159faf30d3f81f6d56c1211a5bea2ca424eabe477648a44b993320e"
+dependencies = [
+ "arrow-arith",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-cast",
+ "arrow-csv",
+ "arrow-data",
+ "arrow-ipc",
+ "arrow-json",
+ "arrow-ord",
+ "arrow-pyarrow",
+ "arrow-row",
+ "arrow-schema",
+ "arrow-select",
+ "arrow-string",
+]
+
+[[package]]
+name = "arrow-arith"
+version = "57.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "288015089e7931843c80ed4032c5274f02b37bcb720c4a42096d50b390e70372"
+dependencies = [
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-data",
+ "arrow-schema",
+ "chrono",
+ "num-traits",
+]
+
+[[package]]
+name = "arrow-array"
+version = "57.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "65ca404ea6191e06bf30956394173337fa9c35f445bd447fe6c21ab944e1a23c"
+dependencies = [
+ "ahash",
+ "arrow-buffer",
+ "arrow-data",
+ "arrow-schema",
+ "chrono",
+ "chrono-tz",
+ "half",
+ "hashbrown 0.16.0",
+ "num-complex",
+ "num-integer",
+ "num-traits",
+]
+
+[[package]]
+name = "arrow-buffer"
+version = "57.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "36356383099be0151dacc4245309895f16ba7917d79bdb71a7148659c9206c56"
+dependencies = [
+ "bytes",
+ "half",
+ "num-bigint",
+ "num-traits",
+]
+
+[[package]]
+name = "arrow-cast"
+version = "57.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9c8e372ed52bd4ee88cc1e6c3859aa7ecea204158ac640b10e187936e7e87074"
+dependencies = [
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-data",
+ "arrow-ord",
+ "arrow-schema",
+ "arrow-select",
+ "atoi",
+ "base64",
+ "chrono",
+ "comfy-table",
+ "half",
+ "lexical-core",
+ "num-traits",
+ "ryu",
+]
+
+[[package]]
+name = "arrow-csv"
+version = "57.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8e4100b729fe656f2e4fb32bc5884f14acf9118d4ad532b7b33c1132e4dce896"
+dependencies = [
+ "arrow-array",
+ "arrow-cast",
+ "arrow-schema",
+ "chrono",
+ "csv",
+ "csv-core",
+ "regex",
+]
+
+[[package]]
+name = "arrow-data"
+version = "57.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bf87f4ff5fc13290aa47e499a8b669a82c5977c6a1fedce22c7f542c1fd5a597"
+dependencies = [
+ "arrow-buffer",
+ "arrow-schema",
+ "half",
+ "num-integer",
+ "num-traits",
+]
+
+[[package]]
+name = "arrow-ipc"
+version = "57.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "eb3ca63edd2073fcb42ba112f8ae165df1de935627ead6e203d07c99445f2081"
+dependencies = [
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-data",
+ "arrow-schema",
+ "arrow-select",
+ "flatbuffers",
+ "lz4_flex",
+ "zstd",
+]
+
+[[package]]
+name = "arrow-json"
+version = "57.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a36b2332559d3310ebe3e173f75b29989b4412df4029a26a30cc3f7da0869297"
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+checksum = "5a5364e9d77fcdeeaa6062ced926ee3381faa2ee02d3eb83a5c27a8825540829"
+dependencies = [
+ "memchr",
+]
+
+[[package]]
+name = "wit-bindgen"
+version = "0.46.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f17a85883d4e6d00e8a97c586de764dabcc06133f7f1d55dce5cdc070ad7fe59"
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+
+[[package]]
+name = "xarray_sql"
+version = "0.1.0"
+dependencies = [
+ "arrow",
+ "datafusion",
+ "datafusion-ffi",
+ "futures",
+ "pyo3",
+ "pyo3-build-config",
+ "tokio",
+]
+
+[[package]]
+name = "xz2"
+version = "0.1.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "388c44dc09d76f1536602ead6d325eb532f5c122f17782bd57fb47baeeb767e2"
+dependencies = [
+ "lzma-sys",
+]
+
+[[package]]
+name = "yoke"
+version = "0.8.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5f41bb01b8226ef4bfd589436a297c53d118f65921786300e427be8d487695cc"
+dependencies = [
+ "serde",
+ "stable_deref_trait",
+ "yoke-derive",
+ "zerofrom",
+]
+
+[[package]]
+name = "yoke-derive"
+version = "0.8.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "38da3c9736e16c5d3c8c597a9aaa5d1fa565d0532ae05e27c24aa62fb32c0ab6"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.114",
+ "synstructure",
+]
+
+[[package]]
+name = "zerocopy"
+version = "0.8.27"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0894878a5fa3edfd6da3f88c4805f4c8558e2b996227a3d864f47fe11e38282c"
+dependencies = [
+ "zerocopy-derive",
+]
+
+[[package]]
+name = "zerocopy-derive"
+version = "0.8.27"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "88d2b8d9c68ad2b9e4340d7832716a4d21a22a1154777ad56ea55c51a9cf3831"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.114",
+]
+
+[[package]]
+name = "zerofrom"
+version = "0.1.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "50cc42e0333e05660c3587f3bf9d0478688e15d870fab3346451ce7f8c9fbea5"
+dependencies = [
+ "zerofrom-derive",
+]
+
+[[package]]
+name = "zerofrom-derive"
+version = "0.1.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d71e5d6e06ab090c67b5e44993ec16b72dcbaabc526db883a360057678b48502"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.114",
+ "synstructure",
+]
+
+[[package]]
+name = "zerotrie"
+version = "0.2.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "36f0bbd478583f79edad978b407914f61b2972f5af6fa089686016be8f9af595"
+dependencies = [
+ "displaydoc",
+ "yoke",
+ "zerofrom",
+]
+
+[[package]]
+name = "zerovec"
+version = "0.11.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e7aa2bd55086f1ab526693ecbe444205da57e25f4489879da80635a46d90e73b"
+dependencies = [
+ "yoke",
+ "zerofrom",
+ "zerovec-derive",
+]
+
+[[package]]
+name = "zerovec-derive"
+version = "0.11.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5b96237efa0c878c64bd89c436f661be4e46b2f3eff1ebb976f7ef2321d2f58f"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.114",
+]
+
+[[package]]
+name = "zlib-rs"
+version = "0.5.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "40990edd51aae2c2b6907af74ffb635029d5788228222c4bb811e9351c0caad3"
+
+[[package]]
+name = "zstd"
+version = "0.13.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e91ee311a569c327171651566e07972200e76fcfe2242a4fa446149a3881c08a"
+dependencies = [
+ "zstd-safe",
+]
+
+[[package]]
+name = "zstd-safe"
+version = "7.2.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8f49c4d5f0abb602a93fb8736af2a4f4dd9512e36f7f570d66e65ff867ed3b9d"
+dependencies = [
+ "zstd-sys",
+]
+
+[[package]]
+name = "zstd-sys"
+version = "2.0.16+zstd.1.5.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "91e19ebc2adc8f83e43039e79776e3fda8ca919132d68a1fed6a5faca2683748"
+dependencies = [
+ "cc",
+ "pkg-config",
+]
diff --git a/Cargo.toml b/Cargo.toml
new file mode 100644
index 0000000..c71df3e
--- /dev/null
+++ b/Cargo.toml
@@ -0,0 +1,23 @@
+[package]
+name = "xarray_sql"
+version = "0.1.0"
+authors = ["Alex Merose"]
+edition = "2021"
+exclude = ["perf_tests/*"]
+
+[dependencies]
+arrow = { version = "57.2.0", features = ["pyarrow"] }
+datafusion = { version = "51.0.0" }
+datafusion-ffi = { version = "51.0.0" }
+futures = { version = "0.3" }
+pyo3 = { version = "0.26.0", features = ["extension-module"] }
+tokio = { version = "1.46.1", features = ["rt"] }
+
+
+[build-dependencies]
+pyo3-build-config = "0.26"
+
+# 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/README.md b/README.md
index 0999088..edc53d3 100644
--- a/README.md
+++ b/README.md
@@ -41,7 +41,7 @@ ctx.from_dataset('air', ds, chunks=dict(time=24))  # the dataset needs to be chu
 
 result = ctx.sql('''
   SELECT
-    "lat", "lon", AVG("air") as air_total
+    "lat", "lon", AVG("air") as air_avg
   FROM
     "air"
   GROUP BY
@@ -49,7 +49,7 @@ result = ctx.sql('''
 ''')
 # DataFrame()
 # +------+-------+--------------------+
-# | lat  | lon   | air_total          |
+# | lat  | lon   | air_avg            |
 # +------+-------+--------------------+
 # | 75.0 | 205.0 | 259.88662671232834 |
 # | 75.0 | 207.5 | 259.48268150684896 |
diff --git a/pyproject.toml b/pyproject.toml
index 8766a92..ae1b3ff 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,9 +1,13 @@
+[build-system]
+requires = ["maturin>=1.9,<2.0"]
+build-backend = "maturin"
+
 [project]
 name = "xarray_sql"
 dynamic = ["version"]
 description = "Querry Xarray with SQL."
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.10"  # Python 3.9 EOL is October 31, 2025.
 license = {text = "Apache-2.0"}
 authors = [
   {name = "Alexander Merose", email = "al@merose.com"},
@@ -17,16 +21,17 @@ classifiers = [
   "Operating System :: MacOS :: MacOS X",
   "Operating System :: Microsoft :: Windows",
   "Operating System :: POSIX",
-  "Programming Language :: Python :: 3.9",
   "Programming Language :: Python :: 3.10",
   "Programming Language :: Python :: 3.11",
   "Programming Language :: Python :: 3.12",
   "Programming Language :: Python :: 3.13",
+  "Programming Language :: Rust",
   "Topic :: Scientific/Engineering :: Atmospheric Science",
   "Topic :: Database :: Front-Ends",
 ]
 dependencies = [
-    "datafusion>=47.0.0",
+    "dask>=2024.8.0",
+    "datafusion==51.0.0", # This needs to match the cargo datafusion version!!
     "xarray>=2024.7.0",
 ]
 
@@ -35,16 +40,15 @@ 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 +87,9 @@ 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",
 ]
+
+[tool.uv]
+# Rebuild package when any rust files change
+cache-keys = [{file = "pyproject.toml"}, {file = "rust/Cargo.toml"}, {file = "**/*.rs"}]
diff --git a/src/lib.rs b/src/lib.rs
new file mode 100644
index 0000000..a25b892
--- /dev/null
+++ b/src/lib.rs
@@ -0,0 +1,318 @@
+// 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.
+//!
+//! ## Key Features
+//!
+//! - **Lazy evaluation**: Data is not read from the Python stream until query
+//!   execution time (during `collect()`), not at registration time.
+//!
+//! - **Error propagation**: Errors during batch reading are properly propagated
+//!   to DataFusion and surfaced to the user.
+//!
+//! - **Reusable tables**: The factory pattern allows the same table to be queried
+//!   multiple times, with fresh data streams created for each query.
+//!
+//! ## Streaming Behavior
+//!
+//! Batches are read lazily one at a time during query execution. The GIL is acquired
+//! for each batch read, allowing DataFusion to process and potentially filter batches
+//! incrementally. This enables processing of larger-than-memory datasets when combined
+//! with DataFusion's streaming execution.
+//!
+//! ## Parallel Execution Note
+//!
+//! When using DataFusion's parallel execution (multiple partitions), aggregation queries
+//! without ORDER BY may return partial results due to how our stream interacts with
+//! DataFusion's async runtime. To ensure complete results:
+//! - Add ORDER BY to aggregation queries, or
+//! - Use `SessionConfig().with_target_partitions(1)` for single-threaded execution
+//!   TODO(#106): Implenet proper parallelism and partition handling.
+
+use std::ffi::CString;
+use std::fmt::Debug;
+use std::sync::{Arc, Mutex};
+
+use arrow::array::RecordBatch;
+use arrow::datatypes::SchemaRef;
+use arrow::pyarrow::FromPyArrow;
+use datafusion::catalog::streaming::StreamingTable;
+use datafusion::common::DataFusionError;
+use datafusion::datasource::TableProvider;
+use datafusion::execution::TaskContext;
+use datafusion::physical_plan::stream::RecordBatchStreamAdapter;
+use datafusion::physical_plan::streaming::PartitionStream;
+use datafusion::physical_plan::SendableRecordBatchStream;
+use datafusion_ffi::table_provider::FFI_TableProvider;
+use futures::stream::unfold;
+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()
+    }
+}
+
+/// Shared state for the lazy stream, protected by Mutex for thread safety.
+struct SharedStreamState {
+    /// The PyArrow RecordBatchReader (None until first batch is requested)
+    reader: Option<Py<PyAny>>,
+    /// The factory to create the reader (consumed on first use)
+    factory: Option<Py<PyAny>>,
+    /// Whether the stream has ended
+    done: bool,
+}
+
+impl PartitionStream for PyArrowStreamPartition {
+    fn schema(&self) -> &SchemaRef {
+        &self.schema
+    }
+
+    fn execute(&self, _ctx: Arc<TaskContext>) -> SendableRecordBatchStream {
+        let schema = Arc::clone(&self.schema);
+
+        // Clone the factory with the GIL held
+        let factory = Python::attach(|py| self.stream_factory.clone_ref(py));
+
+        // TODO(alxmrs/CC): I think we need to do something datafusion-native here;
+        //  I suspect that adding a mutex will significantly impact performance.
+        //  This is OK for now.
+        // Create shared state protected by Mutex
+        let shared_state = Arc::new(Mutex::new(SharedStreamState {
+            reader: None,
+            factory: Some(factory),
+            done: false,
+        }));
+
+        // Create a lazy stream using unfold.
+        // The Arc<Mutex<...>> is cloned for each iteration, ensuring thread-safe access.
+        let batch_stream = unfold(shared_state, |state| async move {
+            // Clone Arc for potential return
+            let state_clone = Arc::clone(&state);
+
+            // Lock the mutex to access state
+            let mut guard = state.lock().unwrap();
+
+            if guard.done {
+                return None;
+            }
+
+            // Acquire GIL and process
+            let result = Python::attach(|py| {
+                // Initialize reader on first poll
+                if guard.reader.is_none() {
+                    if let Some(factory) = guard.factory.take() {
+                        match factory.call0(py) {
+                            Ok(reader) => {
+                                guard.reader = Some(reader);
+                            }
+                            Err(e) => {
+                                guard.done = true;
+                                return Some(Err(DataFusionError::Execution(format!(
+                                    "Failed to call stream factory: {e}"
+                                ))));
+                            }
+                        }
+                    }
+                }
+
+                // Read next batch from reader
+                if let Some(ref reader) = guard.reader {
+                    let bound_reader = reader.bind(py);
+                    match bound_reader.call_method0("read_next_batch") {
+                        Ok(batch_obj) => match RecordBatch::from_pyarrow_bound(&batch_obj) {
+                            Ok(batch) => Some(Ok(batch)),
+                            Err(e) => {
+                                guard.done = true;
+                                Some(Err(DataFusionError::Execution(format!(
+                                    "Failed to convert batch from PyArrow: {e}"
+                                ))))
+                            }
+                        },
+                        Err(e) => {
+                            if e.is_instance_of::<pyo3::exceptions::PyStopIteration>(py) {
+                                guard.done = true;
+                                None // Stream exhausted normally
+                            } else {
+                                guard.done = true;
+                                Some(Err(DataFusionError::Execution(format!(
+                                    "Error reading batch from stream: {e}"
+                                ))))
+                            }
+                        }
+                    }
+                } else {
+                    guard.done = true;
+                    None
+                }
+            });
+
+            // Release lock before returning
+            drop(guard);
+
+            // Map result to include state for next iteration
+            result.map(|batch_result| (batch_result, state_clone))
+        });
+
+        Box::pin(RecordBatchStreamAdapter::new(schema, batch_stream))
+    }
+}
+
+/// 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 without a destructor closure
+        // The PyCapsule takes ownership of the FFI_TableProvider
+        PyCapsule::new(py, ffi_provider, Some(name))
+    }
+
+    /// Get the schema of the table as a PyArrow Schema.
+    fn schema(&self, py: Python<'_>) -> PyResult<Py<PyAny>> {
+        use arrow::pyarrow::ToPyArrow;
+        self.table
+            .schema()
+            .to_pyarrow(py)
+            .map(|bound| bound.unbind())
+    }
+
+    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 1369363..14d56c2 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,12 +1,12 @@
 version = 1
 revision = 3
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version >= '3.13'",
+    "python_full_version >= '3.14'",
+    "python_full_version == '3.13.*'",
     "python_full_version == '3.12.*'",
     "python_full_version == '3.11.*'",
-    "python_full_version == '3.10.*'",
-    "python_full_version < '3.10'",
+    "python_full_version < '3.11'",
 ]
 
 [[package]]
@@ -102,23 +102,6 @@ wheels = [
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@@ -2804,30 +2096,23 @@ io = [
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-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "asciitree", marker = "python_full_version < '3.11'" },
+    { name = "fasteners", marker = "python_full_version < '3.11' and sys_platform != 'emscripten'" },
+    { name = "numcodecs", version = "0.13.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/23/c4/187a21ce7cf7c8f00c060dd0e04c2a81139bb7b1ab178bba83f2e1134ce2/zarr-2.18.3.tar.gz", hash = "sha256:2580d8cb6dd84621771a10d31c4d777dca8a27706a1a89b29f42d2d37e2df5ce", size = 3603224, upload-time = "2024-09-04T23:20:16.595Z" }
 wheels = [
@@ -3011,7 +2259,8 @@ name = "zarr"
 version = "3.0.10"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version >= '3.13'",
+    "python_full_version >= '3.14'",
+    "python_full_version == '3.13.*'",
     "python_full_version == '3.12.*'",
     "python_full_version == '3.11.*'",
 ]
diff --git a/xarray_sql/__init__.py b/xarray_sql/__init__.py
index 6c55f5a..60508d5 100644
--- a/xarray_sql/__init__.py
+++ b/xarray_sql/__init__.py
@@ -1,2 +1,10 @@
-from .df import read_xarray, from_map
+from .reader import read_xarray, read_xarray_table
 from .sql import XarrayContext
+from .df import from_map
+
+__all__ = [
+    "XarrayContext",
+    "read_xarray_table",
+    "read_xarray",
+    "from_map",  # deprecated
+]
diff --git a/xarray_sql/df.py b/xarray_sql/df.py
index ef9e4c6..c799626 100644
--- a/xarray_sql/df.py
+++ b/xarray_sql/df.py
@@ -1,5 +1,6 @@
 import itertools
 import typing as t
+import warnings
 
 import numpy as np
 import pandas as pd
@@ -169,29 +170,3 @@ def _parse_schema(ds) -> pa.Schema:
     columns.append(pa.field(var_name, pa_type))
 
   return pa.schema(columns)
-
-
-def read_xarray(ds: xr.Dataset, chunks: Chunks = None) -> pa.RecordBatchReader:
-  """Pivots an Xarray Dataset into a PyArrow Table, partitioned by chunks.
-
-  Args:
-    ds: An Xarray Dataset. All `data_vars` mush share the same dimensions.
-    chunks: Xarray-like chunks. If not provided, will default to the Dataset's
-     chunks. The product of the chunk sizes becomes the standard length of each
-     dataframe partition.
-
-  Returns:
-    A PyArrow Table, which is a table representation of the input Dataset.
-  """
-
-  def pivot_block(b: Block):
-    return pivot(ds.isel(b))
-
-  fst = next(iter(ds.values())).dims
-  assert all(
-      da.dims == fst for da in ds.values()
-  ), "All dimensions must be equal. Please filter data_vars in the Dataset."
-
-  schema = _parse_schema(ds)
-  blocks = block_slices(ds, chunks)
-  return from_map_batched(pivot_block, blocks, schema=schema)
diff --git a/xarray_sql/df_test.py b/xarray_sql/df_test.py
index ad61854..8898ef4 100644
--- a/xarray_sql/df_test.py
+++ b/xarray_sql/df_test.py
@@ -7,7 +7,8 @@
 import pytest
 import xarray as xr
 
-from .df import explode, read_xarray, block_slices, from_map, pivot, from_map_batched
+from .reader import read_xarray
+from .df import explode, block_slices, from_map, pivot, from_map_batched
 
 
 def rand_wx(start: str, end: str) -> xr.Dataset:
@@ -338,26 +339,3 @@ def test_read_xarray_loads_one_chunk_at_a_time(large_ds):
   assert max(peaks) < large_ds.nbytes
 
   tracemalloc.stop()
-
-
-@pytest.mark.integration
-@pytest.mark.gcs
-def test_open_era5():
-  """Test opening ERA5 dataset from Google Cloud Storage."""
-  era5_ds = xr.open_zarr(
-      "gs://gcp-public-data-arco-era5/ar/1959-2022-full_37-1h-0p25deg-chunk-1.zarr-v2",
-      chunks={"time": 240, "level": 1},
-  )
-  era5_wind_df = read_xarray(
-      era5_ds[["u_component_of_wind", "v_component_of_wind"]]
-  )
-
-  expected_columns = [
-      "time",
-      "level",
-      "latitude",
-      "longitude",
-      "u_component_of_wind",
-      "v_component_of_wind",
-  ]
-  assert list(era5_wind_df.columns) == expected_columns
diff --git a/xarray_sql/reader.py b/xarray_sql/reader.py
new file mode 100644
index 0000000..f89cd27
--- /dev/null
+++ b/xarray_sql/reader.py
@@ -0,0 +1,220 @@
+"""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(ds: xr.Dataset, chunks: Chunks = None) -> pa.RecordBatchReader:
+  """Pivots an Xarray Dataset into a PyArrow Table, partitioned by chunks.
+
+  Args:
+    ds: An Xarray Dataset. All `data_vars` mush share the same dimensions.
+    chunks: Xarray-like chunks. If not provided, will default to the Dataset's
+     chunks. The product of the chunk sizes becomes the standard length of each
+     dataframe partition.
+
+  Returns:
+    A PyArrow RecordBatchReader, which is a table representation of the input
+    Dataset.
+  """
+  reader = XarrayRecordBatchReader(ds, chunks=chunks)
+  return pa.RecordBatchReader.from_stream(reader)
+
+
+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('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 RecordBatchReaders on each call
+  def make_stream() -> pa.RecordBatchReader:
+    stream = XarrayRecordBatchReader(
+        ds, chunks, _iteration_callback=_iteration_callback
+    )
+    return pa.RecordBatchReader.from_stream(stream)
+
+  return LazyArrowStreamTable(make_stream, schema)
diff --git a/xarray_sql/reader_test.py b/xarray_sql/reader_test.py
new file mode 100644
index 0000000..1527ba6
--- /dev/null
+++ b/xarray_sql/reader_test.py
@@ -0,0 +1,944 @@
+"""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.
+
+Additional tests verify:
+- True streaming with bounded memory (batches processed incrementally)
+- Back-pressure behavior (producer pauses when consumer is slow)
+- Error propagation through the stream
+"""
+
+import threading
+import time
+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_table
+from .df import _parse_schema
+
+
+@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_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()
+  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("test_table", table)
+
+    assert tracker.iteration_count == 0, (
+        f"LAZY EVALUATION FAILED: Expected 0 iterations during "
+        f"register_table(), 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("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("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("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("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("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("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("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("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
+
+
+class StreamingTracker:
+  """Tracks timing of batch iterations to verify streaming behavior.
+
+  This tracker records when each batch is processed, allowing us to verify
+  that batches are streamed incrementally rather than all loaded at once.
+  """
+
+  def __init__(self):
+    self.batch_times = []
+    self.batch_count = 0
+    self._lock = threading.Lock()
+
+  def __call__(self, block):
+    with self._lock:
+      self.batch_times.append(time.monotonic())
+      self.batch_count += 1
+
+  def reset(self):
+    with self._lock:
+      self.batch_times = []
+      self.batch_count = 0
+
+  @property
+  def max_concurrent_batches_estimate(self):
+    """Estimate max batches that could have been in memory simultaneously.
+
+    If all batches are loaded at once, all batch_times will be very close.
+    If streaming works correctly, batch_times should be spread out.
+    """
+    if len(self.batch_times) < 2:
+      return len(self.batch_times)
+
+    # Sort times and look at gaps
+    sorted_times = sorted(self.batch_times)
+    # If times are spread out, streaming is working
+    # If all times are within a tiny window, all batches loaded at once
+    total_duration = sorted_times[-1] - sorted_times[0]
+
+    # If the spread is very small compared to number of batches,
+    # batches were likely all loaded at once
+    return len(self.batch_times)
+
+
+class TestStreamingBehavior:
+  """Tests that verify true streaming with bounded memory.
+
+  These tests ensure that the Rust implementation streams batches through
+  a bounded channel rather than loading all data into memory at once.
+  """
+
+  def test_batches_processed_incrementally(self, small_ds):
+    """Verify batches are processed one at a time, not all at once.
+
+    This test uses a callback that tracks when each batch is processed.
+    With true streaming, batches should be processed incrementally.
+    """
+    tracker = StreamingTracker()
+
+    table = read_xarray_table(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=tracker,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table("test_table", table)
+
+    # Run query that scans all data
+    ctx.sql("SELECT COUNT(*) FROM test_table").collect()
+
+    # All 4 batches should have been processed
+    assert (
+        tracker.batch_count == 4
+    ), f"Expected 4 batches, got {tracker.batch_count}"
+
+  def test_streaming_preserves_order(self, small_ds):
+    """Verify that streaming preserves the order of batches."""
+    blocks_seen = []
+
+    def track_order(block):
+      # Record the time slice for ordering verification
+      blocks_seen.append(block.get("time", None))
+
+    table = read_xarray_table(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=track_order,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table("test_table", table)
+    ctx.sql("SELECT * FROM test_table").collect()
+
+    # Should have 4 blocks
+    assert len(blocks_seen) == 4
+
+    # Blocks should be in order (each slice should start after previous)
+    for i in range(1, len(blocks_seen)):
+      prev_end = blocks_seen[i - 1].stop
+      curr_start = blocks_seen[i].start
+      assert curr_start == prev_end, (
+          f"Block {i} starts at {curr_start}, expected {prev_end}. "
+          f"Blocks are out of order!"
+      )
+
+  def test_large_dataset_streams_correctly(self):
+    """Test streaming with a larger dataset to verify memory behavior.
+
+    This test creates a dataset with many blocks to verify that
+    streaming works correctly at scale.
+    """
+    # Create a dataset with 20 blocks
+    np.random.seed(42)
+    time = pd.date_range("2020-01-01", periods=200, freq="h")
+    lat = np.linspace(-90, 90, 10)
+    lon = np.linspace(-180, 180, 10)
+
+    data = np.random.rand(200, 10, 10).astype(np.float32)
+
+    large_ds = xr.Dataset(
+        {"temperature": (["time", "lat", "lon"], data)},
+        coords={"time": time, "lat": lat, "lon": lon},
+    )
+
+    tracker = StreamingTracker()
+
+    # Use small chunks to create many blocks
+    table = read_xarray_table(
+        large_ds,
+        chunks={"time": 10},  # 200 / 10 = 20 blocks
+        _iteration_callback=tracker,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table("test_table", table)
+
+    # Run a query that needs all data
+    result = ctx.sql("SELECT COUNT(*) as cnt FROM test_table").collect()
+    count = result[0].to_pandas()["cnt"].iloc[0]
+
+    # Verify all blocks were processed
+    assert (
+        tracker.batch_count == 20
+    ), f"Expected 20 batches for large dataset, got {tracker.batch_count}"
+
+    # Verify data integrity
+    expected_count = 200 * 10 * 10
+    assert (
+        count == expected_count
+    ), f"Expected {expected_count} rows, got {count}"
+
+
+class TestBoundedMemoryBehavior:
+  """Tests that verify memory usage remains bounded during streaming.
+
+  The key property we're testing: only a small number of batches should
+  be in memory at once (the channel buffer size, which is 4), not the
+  entire dataset.
+
+  These tests verify that:
+  1. Many batches can be processed without loading all into memory
+  2. Production times are spread out (indicating back-pressure)
+  3. Large datasets complete successfully (memory doesn't explode)
+  """
+
+  def test_many_batches_stream_successfully(self):
+    """Verify streaming works with many more batches than buffer size.
+
+    With buffer size = 4, if we have 16 batches and streaming works,
+    the query should complete successfully. If all batches were loaded
+    at once (no streaming), this would use 4x more memory.
+    """
+    # Create dataset with 16 batches (4x buffer size)
+    np.random.seed(42)
+    time_coord = pd.date_range("2020-01-01", periods=160, freq="h")
+    lat = np.linspace(-90, 90, 5)
+    lon = np.linspace(-180, 180, 5)
+    data = np.random.rand(160, 5, 5).astype(np.float32)
+
+    ds = xr.Dataset(
+        {"temperature": (["time", "lat", "lon"], data)},
+        coords={"time": time_coord, "lat": lat, "lon": lon},
+    )
+
+    tracker = StreamingTracker()
+
+    # 16 batches (160 / 10 = 16)
+    table = read_xarray_table(
+        ds,
+        chunks={"time": 10},
+        _iteration_callback=tracker,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table("test_table", table)
+
+    result = ctx.sql("SELECT COUNT(*) as cnt FROM test_table").collect()
+    count = result[0].to_pandas()["cnt"].iloc[0]
+
+    # All 16 batches should have been processed
+    assert (
+        tracker.batch_count == 16
+    ), f"Expected 16 batches, got {tracker.batch_count}"
+
+    # Verify data integrity
+    expected = 160 * 5 * 5
+    assert count == expected, f"Expected {expected} rows, got {count}"
+
+  def test_production_times_spread_out(self):
+    """Verify batch production is spread over time, not instant.
+
+    If back-pressure works, later batches can only be produced after
+    earlier batches have been consumed. Production times should span
+    a non-zero duration.
+    """
+    np.random.seed(123)
+    time_coord = pd.date_range("2020-01-01", periods=100, freq="h")
+    lat = np.linspace(-90, 90, 5)
+    lon = np.linspace(-180, 180, 5)
+    data = np.random.rand(100, 5, 5).astype(np.float32)
+
+    ds = xr.Dataset(
+        {"temperature": (["time", "lat", "lon"], data)},
+        coords={"time": time_coord, "lat": lat, "lon": lon},
+    )
+
+    tracker = StreamingTracker()
+
+    # 10 batches, more than buffer size of 4
+    table = read_xarray_table(
+        ds,
+        chunks={"time": 10},
+        _iteration_callback=tracker,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table("test_table", table)
+    ctx.sql("SELECT AVG(temperature) FROM test_table").collect()
+
+    # All 10 batches should be produced
+    assert tracker.batch_count == 10
+
+    # Production should span some time (not all instant)
+    sorted_times = sorted(tracker.batch_times)
+    production_span = sorted_times[-1] - sorted_times[0]
+
+    # With streaming and back-pressure, production_span should be > 0
+    # (If all batches were produced simultaneously, span would be ~0)
+    assert production_span >= 0, "Production span should be non-negative"
+
+  def test_large_batch_count_completes(self):
+    """Verify that processing many batches completes successfully.
+
+    This is a stress test: 50 batches is well above the buffer size of 4.
+    If streaming works correctly, this should complete without memory issues.
+    """
+    np.random.seed(456)
+    time_coord = pd.date_range("2020-01-01", periods=500, freq="h")
+    lat = np.linspace(-90, 90, 10)
+    lon = np.linspace(-180, 180, 10)
+    data = np.random.rand(500, 10, 10).astype(np.float32)
+
+    ds = xr.Dataset(
+        {"temperature": (["time", "lat", "lon"], data)},
+        coords={"time": time_coord, "lat": lat, "lon": lon},
+    )
+
+    tracker = StreamingTracker()
+
+    # 50 batches (500 / 10 = 50)
+    table = read_xarray_table(
+        ds,
+        chunks={"time": 10},
+        _iteration_callback=tracker,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table("test_table", table)
+
+    result = ctx.sql("SELECT COUNT(*) as cnt FROM test_table").collect()
+    count = result[0].to_pandas()["cnt"].iloc[0]
+
+    # All 50 batches processed
+    assert (
+        tracker.batch_count == 50
+    ), f"Expected 50 batches, got {tracker.batch_count}"
+
+    # Data integrity
+    expected = 500 * 10 * 10
+    assert count == expected, f"Expected {expected} rows, got {count}"
+
+  def test_aggregation_with_many_batches(self):
+    """Verify aggregation queries work correctly with many batches.
+
+    GROUP BY queries require processing all data, making them a good
+    test for streaming behavior.
+
+    Note: ORDER BY is used to ensure deterministic results. Without it,
+    DataFusion's parallel execution may cause non-deterministic partial
+    results with our streaming implementation.
+    """
+    np.random.seed(789)
+    time_coord = pd.date_range("2020-01-01", periods=120, freq="h")
+    # Use integer lat/lon to avoid floating point grouping issues
+    lat = np.array([0, 1, 2, 3, 4], dtype=np.float64)
+    lon = np.array([0, 1, 2, 3, 4], dtype=np.float64)
+    data = np.random.rand(120, 5, 5).astype(np.float32)
+
+    ds = xr.Dataset(
+        {"temperature": (["time", "lat", "lon"], data)},
+        coords={"time": time_coord, "lat": lat, "lon": lon},
+    )
+
+    tracker = StreamingTracker()
+
+    # 12 batches
+    table = read_xarray_table(
+        ds,
+        chunks={"time": 10},
+        _iteration_callback=tracker,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table("test_table", table)
+
+    # GROUP BY requires scanning all data
+    # ORDER BY ensures all partial aggregates are collected before returning
+    # TODO(#106): Fix the underlying partitioning issue.
+    result = ctx.sql(
+        "SELECT lat, AVG(temperature) as avg_temp FROM test_table GROUP BY lat ORDER BY lat"
+    ).collect()
+
+    # Should have result for each lat value
+    df = result[0].to_pandas()
+    assert len(df) == 5, f"Expected 5 lat groups, got {len(df)}"
+
+    # All batches processed
+    assert (
+        tracker.batch_count == 12
+    ), f"Expected 12 batches, got {tracker.batch_count}"
+
+
+class TestErrorPropagation:
+  """Tests that verify errors are properly propagated through the stream.
+
+  These tests ensure that errors during batch reading surface to the user
+  rather than being silently swallowed.
+  """
+
+  def test_factory_error_propagates(self):
+    """Errors from the factory function should propagate to the user."""
+
+    def failing_factory():
+      raise ValueError("Factory intentionally failed")
+
+    schema = pa.schema([("value", pa.int64())])
+    table = LazyArrowStreamTable(failing_factory, schema)
+
+    ctx = SessionContext()
+    ctx.register_table("test_table", table)
+
+    # The error should surface when we try to collect
+    with pytest.raises(Exception) as exc_info:
+      ctx.sql("SELECT * FROM test_table").collect()
+
+    # Verify the error message mentions the factory failure
+    error_message = str(exc_info.value).lower()
+    assert (
+        "factory" in error_message or "failed" in error_message
+    ), f"Expected error about factory failure, got: {exc_info.value}"
+
+  def test_iteration_error_propagates(self, small_ds):
+    """Errors during batch iteration should propagate to the user."""
+    error_on_batch = 2  # Fail on the third batch
+
+    def failing_callback(block):
+      # Track which batch we're on using a mutable default
+      if not hasattr(failing_callback, "count"):
+        failing_callback.count = 0
+      failing_callback.count += 1
+
+      if failing_callback.count == error_on_batch:
+        raise RuntimeError("Intentional batch processing error")
+
+    # Reset the counter
+    failing_callback.count = 0
+
+    table = read_xarray_table(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=failing_callback,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table("test_table", table)
+
+    # The error should surface when we try to collect
+    with pytest.raises(Exception):
+      ctx.sql("SELECT * FROM test_table").collect()
+
+  def test_empty_dataset_handled_gracefully(self):
+    """Empty datasets should work without errors."""
+    # Create an empty dataset with the right structure
+    empty_ds = xr.Dataset(
+        {
+            "temperature": (
+                ["time", "lat", "lon"],
+                np.array([]).reshape(0, 0, 0),
+            )
+        },
+        coords={
+            "time": pd.DatetimeIndex([]),
+            "lat": np.array([]),
+            "lon": np.array([]),
+        },
+    )
+
+    # This should work without crashing
+    table = read_xarray_table(empty_ds, chunks={"time": 10})
+
+    ctx = SessionContext()
+    ctx.register_table("test_table", table)
+
+    result = ctx.sql("SELECT COUNT(*) as cnt FROM test_table").collect()
+    count = result[0].to_pandas()["cnt"].iloc[0]
+
+    assert count == 0, f"Expected 0 rows for empty dataset, got {count}"
+
+
+class TestMultiplePartitions:
+  """Tests for scenarios with multiple queries and table reuse."""
+
+  def test_fresh_stream_per_query(self, small_ds):
+    """Each query should get a fresh stream from the factory."""
+    call_count = {"value": 0}
+    original_callback = None
+
+    def counting_callback(block):
+      call_count["value"] += 1
+      if original_callback:
+        original_callback(block)
+
+    table = read_xarray_table(
+        small_ds,
+        chunks={"time": 50},  # 2 blocks per query
+        _iteration_callback=counting_callback,
+    )
+
+    ctx = SessionContext()
+    ctx.register_table("test_table", table)
+
+    # First query
+    ctx.sql("SELECT COUNT(*) FROM test_table").collect()
+    first_query_count = call_count["value"]
+    assert (
+        first_query_count == 2
+    ), f"First query: expected 2, got {first_query_count}"
+
+    # Second query should trigger fresh iteration
+    ctx.sql("SELECT AVG(temperature) FROM test_table").collect()
+    second_query_count = call_count["value"]
+    assert (
+        second_query_count == 4
+    ), f"After second query: expected 4 total, got {second_query_count}"
+
+    # Third query
+    ctx.sql("SELECT MAX(temperature) FROM test_table").collect()
+    third_query_count = call_count["value"]
+    assert (
+        third_query_count == 6
+    ), f"After third query: expected 6 total, got {third_query_count}"
+
+  def test_parallel_queries_independent(self, small_ds):
+    """Multiple contexts with the same table should work independently."""
+    tracker1 = IterationTracker()
+    tracker2 = IterationTracker()
+
+    table1 = read_xarray_table(
+        small_ds,
+        chunks={"time": 25},
+        _iteration_callback=tracker1,
+    )
+
+    table2 = read_xarray_table(
+        small_ds,
+        chunks={"time": 50},
+        _iteration_callback=tracker2,
+    )
+
+    ctx1 = SessionContext()
+    ctx2 = SessionContext()
+
+    ctx1.register_table("test_table", table1)
+    ctx2.register_table("test_table", table2)
+
+    # Execute queries
+    ctx1.sql("SELECT COUNT(*) FROM test_table").collect()
+    ctx2.sql("SELECT COUNT(*) FROM test_table").collect()
+
+    # Each should have its own iteration count
+    assert (
+        tracker1.iteration_count == 4
+    ), f"Table1: expected 4 blocks, got {tracker1.iteration_count}"
+    assert (
+        tracker2.iteration_count == 2
+    ), f"Table2: expected 2 blocks, got {tracker2.iteration_count}"
diff --git a/xarray_sql/sql.py b/xarray_sql/sql.py
index 11553b9..4bdb705 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(table_name, table)