diff --git a/dataframely/_compat.py b/dataframely/_compat.py
index 154ecb88..cd54a34b 100644
--- a/dataframely/_compat.py
+++ b/dataframely/_compat.py
@@ -52,13 +52,6 @@ class PGDialect_psycopg2:  # type: ignore # noqa: N801
     class Dialect:  # type: ignore # noqa: N801
         pass
 
-# -------------------------------------- PYARROW ------------------------------------- #
-
-try:
-    import pyarrow as pa
-except ImportError:  # pragma: no cover
-    pa = _DummyModule("pyarrow")  # type: ignore
-
 # -------------------------------------- PYDANTIC ------------------------------------ #
 
 try:
@@ -97,7 +90,6 @@ class Dialect:  # type: ignore # noqa: N801
     "Dialect",
     "MSDialect_pyodbc",
     "PartitionSchemeOrSinkDirectory",
-    "pa",
     "PGDialect_psycopg2",
     "pydantic_core_schema",
     "pydantic",
diff --git a/dataframely/columns/_base.py b/dataframely/columns/_base.py
index 88628045..938a1449 100644
--- a/dataframely/columns/_base.py
+++ b/dataframely/columns/_base.py
@@ -13,7 +13,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, pydantic, sa, sa_TypeEngine
+from dataframely._compat import pydantic, sa, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
@@ -224,24 +224,6 @@ def sqlalchemy_column(self, name: str, dialect: sa.Dialect) -> sa.Column:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         """The :mod:`sqlalchemy` dtype equivalent of this column data type."""
 
-    # ------------------------------------ PYARROW ----------------------------------- #
-
-    def pyarrow_field(self, name: str) -> pa.Field:
-        """Obtain the pyarrow field of this column definition.
-
-        Args:
-            name: The name of the column.
-
-        Returns:
-            The :mod:`pyarrow` field definition.
-        """
-        return pa.field(name, self.pyarrow_dtype, nullable=self.nullable)
-
-    @property
-    @abstractmethod
-    def pyarrow_dtype(self) -> pa.DataType:
-        """The :mod:`pyarrow` dtype equivalent of this column data type."""
-
     # ----------------------------------- PYDANTIC ----------------------------------- #
 
     def pydantic_field(self) -> Any:
diff --git a/dataframely/columns/any.py b/dataframely/columns/any.py
index 740407da..46030b3c 100644
--- a/dataframely/columns/any.py
+++ b/dataframely/columns/any.py
@@ -7,7 +7,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_mssql, sa_TypeEngine
+from dataframely._compat import sa, sa_mssql, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
@@ -79,13 +79,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:  # pragma: no cover
                 raise NotImplementedError("SQL column cannot have 'Any' type.")
 
-    def pyarrow_field(self, name: str) -> pa.Field:
-        return pa.field(name, self.pyarrow_dtype, nullable=self.nullable)
-
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.null()
-
     @property
     def _python_type(self) -> AnyType:
         return AnyType
diff --git a/dataframely/columns/array.py b/dataframely/columns/array.py
index 87a4b9c5..20aee171 100644
--- a/dataframely/columns/array.py
+++ b/dataframely/columns/array.py
@@ -6,12 +6,11 @@
 import math
 import sys
 import warnings
-from collections.abc import Sequence
 from typing import Any, cast
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely.random import Generator
 
 from ._base import Check, Column
@@ -132,18 +131,6 @@ def _arrow_nullability(self) -> tuple[bool, list[Any]]:
             nullability = (True, [nullability])
         return (self.nullable, [nullability])
 
-    def _pyarrow_field_of_shape(self, shape: Sequence[int]) -> pa.Field:
-        if shape:
-            size, *rest = shape
-            inner_type = self._pyarrow_field_of_shape(rest)
-            return pa.field("item", pa.list_(inner_type, size), nullable=True)
-        else:
-            return self.inner.pyarrow_field("item")
-
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return self._pyarrow_field_of_shape(self.shape).type
-
     @property
     def _python_type(self) -> Any:
         inner_type = self.inner.pydantic_field()
diff --git a/dataframely/columns/binary.py b/dataframely/columns/binary.py
index 888d1bd3..1845580c 100644
--- a/dataframely/columns/binary.py
+++ b/dataframely/columns/binary.py
@@ -7,7 +7,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely.random import Generator
 
 from ._base import Column
@@ -29,10 +29,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:
                 return sa.LargeBinary()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.large_binary()
-
     @property
     def _python_type(self) -> Any:
         return bytes
diff --git a/dataframely/columns/bool.py b/dataframely/columns/bool.py
index a1e48d10..14a30a24 100644
--- a/dataframely/columns/bool.py
+++ b/dataframely/columns/bool.py
@@ -7,7 +7,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely.random import Generator
 
 from ._base import Column
@@ -25,10 +25,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.Boolean()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.bool_()
-
     @property
     def _python_type(self) -> Any:
         return bool
diff --git a/dataframely/columns/categorical.py b/dataframely/columns/categorical.py
index aea7412c..6d63f388 100644
--- a/dataframely/columns/categorical.py
+++ b/dataframely/columns/categorical.py
@@ -7,7 +7,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely.random import Generator
 
 from ._base import Check, Column
@@ -79,10 +79,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.String()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.dictionary(pa.uint32(), pa.large_string())
-
     @property
     def _python_type(self) -> Any:
         return str
diff --git a/dataframely/columns/datetime.py b/dataframely/columns/datetime.py
index 705a1058..9c6bc169 100644
--- a/dataframely/columns/datetime.py
+++ b/dataframely/columns/datetime.py
@@ -10,7 +10,7 @@
 import polars as pl
 from polars._typing import TimeUnit
 
-from dataframely._compat import pa, sa, sa_mssql, sa_TypeEngine
+from dataframely._compat import sa, sa_mssql, sa_TypeEngine
 from dataframely._polars import (
     EPOCH_DATETIME,
     date_matches_resolution,
@@ -141,10 +141,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:
                 return sa.Date()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.date32()
-
     @property
     def _python_type(self) -> Any:
         return dt.date
@@ -292,10 +288,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:
                 return sa.Time()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.time64("ns")
-
     @property
     def _python_type(self) -> Any:
         return dt.time
@@ -442,15 +434,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:
                 return sa.DateTime(timezone=timezone_enabled)
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        time_zone = (
-            self.time_zone.tzname(None)
-            if isinstance(self.time_zone, dt.tzinfo)
-            else self.time_zone
-        )
-        return pa.timestamp(self.time_unit, time_zone)
-
     @property
     def _python_type(self) -> Any:
         return dt.datetime
@@ -612,10 +595,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:
                 return sa.Interval()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.duration(self.time_unit)
-
     @property
     def _python_type(self) -> Any:
         return dt.timedelta
diff --git a/dataframely/columns/decimal.py b/dataframely/columns/decimal.py
index a160c17c..673f9cd7 100644
--- a/dataframely/columns/decimal.py
+++ b/dataframely/columns/decimal.py
@@ -8,7 +8,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
@@ -133,13 +133,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         else:
             return sa.Numeric(self.precision, self.scale)
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        # PyArrow requires an explicit value for precision.
-        # If precision is None, we pass decimal128's maximum precision of 38 to be safe.
-        # We do not use decimal256 since its values cannot be represented in SQL Server.
-        return pa.decimal128(self.precision or 38, self.scale)
-
     @property
     def _python_type(self) -> Any:
         return decimal.Decimal
diff --git a/dataframely/columns/enum.py b/dataframely/columns/enum.py
index e6f7336a..02743d67 100644
--- a/dataframely/columns/enum.py
+++ b/dataframely/columns/enum.py
@@ -10,7 +10,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
@@ -130,16 +130,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             return sa.CHAR(category_lengths[0])
         return sa.String(max(category_lengths))
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        if len(self.categories) <= 2**8 - 1:
-            dtype = pa.uint8()
-        elif len(self.categories) <= 2**16 - 1:
-            dtype = pa.uint16()
-        else:
-            dtype = pa.uint32()
-        return pa.dictionary(dtype, pa.large_string(), ordered=True)
-
     @property
     def _python_type(self) -> Any:
         return Literal[tuple(self.categories)]
diff --git a/dataframely/columns/float.py b/dataframely/columns/float.py
index 77c28531..c43f30d7 100644
--- a/dataframely/columns/float.py
+++ b/dataframely/columns/float.py
@@ -13,7 +13,7 @@
 import polars as pl
 from polars.datatypes.group import FLOAT_DTYPES
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
@@ -193,10 +193,6 @@ def validate_dtype(self, dtype: PolarsDataType) -> bool:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.Float()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.float64()
-
     @classproperty
     def max_value(self) -> float:
         return float(np.finfo(np.float64).max)
@@ -217,10 +213,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.REAL()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.float32()
-
     @classproperty
     def max_value(self) -> float:
         return float(np.finfo(np.float32).max)
@@ -241,10 +233,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.Float()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.float64()
-
     @classproperty
     def max_value(self) -> float:
         return float(np.finfo(np.float64).max)
diff --git a/dataframely/columns/integer.py b/dataframely/columns/integer.py
index f7ceca79..1d6e3a07 100644
--- a/dataframely/columns/integer.py
+++ b/dataframely/columns/integer.py
@@ -10,7 +10,7 @@
 import polars as pl
 from polars.datatypes.group import INTEGER_DTYPES
 
-from dataframely._compat import pa, sa, sa_mssql, sa_TypeEngine
+from dataframely._compat import sa, sa_mssql, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
@@ -186,10 +186,6 @@ def validate_dtype(self, dtype: PolarsDataType) -> bool:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.Integer()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.int64()
-
     @classproperty
     def num_bytes(self) -> int:
         return 8
@@ -210,10 +206,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.SmallInteger()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.int8()
-
     @classproperty
     def num_bytes(self) -> int:
         return 1
@@ -234,10 +226,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.SmallInteger()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.int16()
-
     @classproperty
     def num_bytes(self) -> int:
         return 2
@@ -258,10 +246,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.Integer()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.int32()
-
     @classproperty
     def num_bytes(self) -> int:
         return 4
@@ -282,10 +266,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.BigInteger()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.int64()
-
     @classproperty
     def num_bytes(self) -> int:
         return 8
@@ -312,10 +292,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:
                 return sa.SmallInteger()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.uint8()
-
     @classproperty
     def num_bytes(self) -> int:
         return 1
@@ -336,10 +312,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.Integer()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.uint16()
-
     @classproperty
     def num_bytes(self) -> int:
         return 2
@@ -360,10 +332,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.BigInteger()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.uint32()
-
     @classproperty
     def num_bytes(self) -> int:
         return 4
@@ -384,10 +352,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.BigInteger()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.uint64()
-
     @classproperty
     def num_bytes(self) -> int:
         return 8
diff --git a/dataframely/columns/list.py b/dataframely/columns/list.py
index 9529ce01..e6fa0de6 100644
--- a/dataframely/columns/list.py
+++ b/dataframely/columns/list.py
@@ -11,7 +11,7 @@
 from polars.expr.array import ExprArrayNameSpace
 from polars.expr.list import ExprListNameSpace
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
@@ -147,11 +147,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
     def _arrow_nullability(self) -> tuple[bool, list[Any]]:
         return (self.nullable, [self.inner._arrow_nullability()])
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        # NOTE: Polars uses `large_list`s by default.
-        return pa.large_list(self.inner.pyarrow_field("item"))
-
     @property
     def _python_type(self) -> Any:
         inner_type = self.inner.pydantic_field()
diff --git a/dataframely/columns/object.py b/dataframely/columns/object.py
index e157e95b..2a92d370 100644
--- a/dataframely/columns/object.py
+++ b/dataframely/columns/object.py
@@ -7,7 +7,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely.random import Generator
 
 from ._base import Check, Column
@@ -67,10 +67,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         raise NotImplementedError("SQL column cannot have 'Object' type.")
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        raise NotImplementedError("PyArrow column cannot have 'Object' type.")
-
     @property
     def _python_type(self) -> Any:
         return Any
diff --git a/dataframely/columns/string.py b/dataframely/columns/string.py
index cce4523c..39558fd2 100644
--- a/dataframely/columns/string.py
+++ b/dataframely/columns/string.py
@@ -7,7 +7,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely._native import regex_matching_string_length
 from dataframely.random import Generator
 
@@ -120,10 +120,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
                 return sa.String(max_length)
         return sa.String()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.large_string()
-
     @property
     def _python_type(self) -> Any:
         return str
diff --git a/dataframely/columns/struct.py b/dataframely/columns/struct.py
index 88c3936c..c4d4ea52 100644
--- a/dataframely/columns/struct.py
+++ b/dataframely/columns/struct.py
@@ -8,7 +8,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, pydantic, sa, sa_postgresql, sa_TypeEngine
+from dataframely._compat import pydantic, sa, sa_postgresql, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
@@ -131,10 +131,6 @@ def _arrow_nullability(self) -> tuple[bool, list[Any]]:
             [col._arrow_nullability() for col in self.inner.values()],
         )
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.struct([col.pyarrow_field(name) for name, col in self.inner.items()])
-
     @property
     def _python_type(self) -> Any:
         fields = {name: col.pydantic_field() for name, col in self.inner.items()}
diff --git a/dataframely/schema.py b/dataframely/schema.py
index dab8d151..15602a3c 100644
--- a/dataframely/schema.py
+++ b/dataframely/schema.py
@@ -17,7 +17,7 @@
 from polars._typing import FileSource
 
 from ._base_schema import ORIGINAL_COLUMN_PREFIX, BaseSchema
-from ._compat import PartitionSchemeOrSinkDirectory, deltalake, pa, pydantic, sa
+from ._compat import PartitionSchemeOrSinkDirectory, deltalake, pydantic, sa
 from ._deprecation import deprecated
 from ._match_to_schema import match_to_schema
 from ._native import format_rule_failures
@@ -1411,17 +1411,6 @@ def to_sqlalchemy_columns(cls, dialect: sa.Dialect) -> list[sa.Column]:
             col.sqlalchemy_column(name, dialect) for name, col in cls.columns().items()
         ]
 
-    @classmethod
-    def to_pyarrow_schema(cls) -> pa.Schema:
-        """Obtain the pyarrow schema for this schema.
-
-        Returns:
-            A :mod:`pyarrow` schema that mirrors the schema defined by this class.
-        """
-        return pa.schema(
-            [col.pyarrow_field(name) for name, col in cls.columns().items()]
-        )
-
     @classmethod
     def to_pydantic_model(cls, name: str | None = None) -> type[pydantic.BaseModel]:
         """Convert this schema to a pydantic model.
diff --git a/docs/_templates/classes/column.rst b/docs/_templates/classes/column.rst
index 032988b5..f72c56bb 100644
--- a/docs/_templates/classes/column.rst
+++ b/docs/_templates/classes/column.rst
@@ -8,6 +8,6 @@
 
 .. autoclass:: {{ name }}
     :members:
-    :exclude-members: as_dict, from_dict, matches, pyarrow_field, pyarrow_dtype, sqlalchemy_dtype, sqlalchemy_column, validate_dtype, validation_rules
+    :exclude-members: as_dict, from_dict, matches, sqlalchemy_dtype, sqlalchemy_column, validate_dtype, validation_rules
     :autosummary:
     :autosummary-nosignatures:
diff --git a/docs/api/schema/conversion.rst b/docs/api/schema/conversion.rst
index 70906514..074049c6 100644
--- a/docs/api/schema/conversion.rst
+++ b/docs/api/schema/conversion.rst
@@ -7,5 +7,4 @@ Conversion
     :toctree: _gen/
 
     Schema.to_sqlalchemy_columns
-    Schema.to_pyarrow_schema
     Schema.to_polars_schema
diff --git a/docs/index.md b/docs/index.md
index ae6bb08b..1974ccd3 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -15,7 +15,7 @@ schema information to data frame type hints.
 - Introspect validation failure information for run-time failures
 - Enhanced type hints for validated data frames allowing users to clearly express expectations about inputs and
   outputs (i.e., contracts) in data pipelines
-- Integrate schemas with external tools (e.g., `sqlalchemy`, `pyarrow`, or via the
+- Integrate schemas with external tools (e.g., `sqlalchemy`, `pydantic`, or via the
   [Arrow PyCapsule](https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html#arrowschema-export))
 - Generate test data that comply with a schema or collection of schemas and its validation rules
 
diff --git a/pixi.lock b/pixi.lock
index 4ae1e70b..cb35ba64 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,10 +1,31 @@
 version: 7
 platforms:
 - name: linux-64
+  virtual-packages:
+  - __unix=0=0
+  - __linux=4.18
+  - __glibc=2.28
+  - __archspec=0=x86_64
 - name: linux-aarch64
+  virtual-packages:
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+  - __linux=4.18
+  - __glibc=2.28
+  - __archspec=0=aarch64
 - name: osx-64
+  virtual-packages:
+  - __unix=0=0
+  - __osx=13.0
+  - __archspec=0=x86_64
 - name: osx-arm64
+  virtual-packages:
+  - __unix=0=0
+  - __osx=13.0
+  - __archspec=0=m1
 - name: win-64
+  virtual-packages:
+  - __win=10.0
+  - __archspec=0=x86_64
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@@ -5535,53 +5556,117 @@ environments:
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+  - libedit >=3.1.20250104,<4.0a0
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+  - openssl >=3.5.7,<4.0a0
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@@ -15314,6 +18447,23 @@ packages:
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   size: 1401836
   timestamp: 1770863223557
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+  - libabseil-static =20260526.0=cxx17*
+  license: Apache-2.0
+  license_family: Apache
+  run_exports:
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   timestamp: 1780065235793
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+  - libgoogle-cloud-storage >=3.7.0,<3.8.0a0
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+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libarrow-dataset-25.0.0-hb326ee9_3_cpu.conda
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+  - libarrow-compute 25.0.0 hdd99842_3_cpu
+  - libgcc >=14
+  - libparquet 25.0.0 h87079af_3_cpu
+  - libstdcxx >=14
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libarrow-substrait-24.0.0-hf29bd21_4_cpu.conda
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+  - libarrow-dataset 25.0.0 hb326ee9_3_cpu
+  - libgcc >=14
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+  - libstdcxx >=14
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlidec-1.2.0-he30d5cf_1.conda
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlienc-1.2.0-he30d5cf_1.conda
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.11.0-8_hd72aa62_openblas.conda
@@ -15478,6 +18748,9 @@ packages:
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   timestamp: 1777461485901
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   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
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+  - libprotobuf >=7.35.1,<7.35.2.0a0
+  - libstdcxx >=14
+  - openssl >=3.5.7,<4.0a0
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+  license: Apache-2.0
+  license_family: Apache
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@@ -15659,11 +19000,34 @@ packages:
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   - re2
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-  - grpc-cpp =1.78.1
+  - grpc-cpp =1.78.1
+  license: Apache-2.0
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+  - libstdcxx >=14
+  - libzlib >=1.3.2,<2.0a0
+  - openssl >=3.5.7,<4.0a0
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   license: Apache-2.0
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-  size: 6670294
-  timestamp: 1774012507815
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@@ -15718,6 +19082,9 @@ packages:
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+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libopentelemetry-cpp-1.27.0-hbe47268_1.conda
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+  - libzlib >=1.3.2,<2.0a0
+  - nlohmann_json
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+  license: Apache-2.0
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@@ -15768,6 +19157,14 @@ packages:
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+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libopentelemetry-cpp-headers-1.27.0-h8af1aa0_1.conda
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   license_family: BSD
-  size: 117017
-  timestamp: 1718284325443
+  size: 117017
+  timestamp: 1718284325443
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+  md5: 18b5548f81de9790deb92f1523a2ae4a
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+  - gflags >=2.3.0,<2.4.0a0
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+  license: BSD-3-Clause
+  license_family: BSD
+  run_exports:
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   size: 1193620
   timestamp: 1769770267475
+- conda: https://conda.anaconda.org/conda-forge/osx-64/krb5-1.22.2-h3ddfcb2_1.conda
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+  - libedit >=3.1.20250104,<4.0a0
+  - openssl >=3.5.7,<4.0a0
+  license: MIT
+  license_family: MIT
+  run_exports:
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+    - krb5 >=1.22.2,<1.23.0a0
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/lazy-object-proxy-1.12.0-py310hd2d5e8d_1.conda
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@@ -21975,6 +26008,23 @@ packages:
   license_family: Apache
   size: 1217836
   timestamp: 1770863510112
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libabseil-20260526.0-cxx17_h1a06613_1.conda
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+  md5: 89d5ad48e1c61baf6bb05ebc15556572
+  depends:
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+  - libcxx >=19
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+  - abseil-cpp =20260526.0
+  license: Apache-2.0
+  license_family: Apache
+  run_exports:
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+    - libabseil >=20260526.0,<20260527.0a0
+    - libabseil =*=cxx17*
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@@ -22011,6 +26061,45 @@ packages:
   license_family: APACHE
   size: 4366653
   timestamp: 1779973491000
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-25.0.0-h5ff653a_3_cpu.conda
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+  - azure-core-cpp >=1.16.3,<1.16.4.0a0
+  - azure-identity-cpp >=1.13.3,<1.13.4.0a0
+  - azure-storage-blobs-cpp >=12.18.0,<12.18.1.0a0
+  - azure-storage-files-datalake-cpp >=12.16.0,<12.16.1.0a0
+  - bzip2 >=1.0.8,<2.0a0
+  - glog >=0.7.1,<0.8.0a0
+  - libabseil * cxx17*
+  - libabseil >=20260526.0,<20260527.0a0
+  - libbrotlidec >=1.2.0,<1.3.0a0
+  - libbrotlienc >=1.2.0,<1.3.0a0
+  - libcxx >=21
+  - libgoogle-cloud >=3.7.0,<3.8.0a0
+  - libgoogle-cloud-storage >=3.7.0,<3.8.0a0
+  - libopentelemetry-cpp >=1.27.0,<1.28.0a0
+  - libprotobuf >=7.35.1,<7.35.2.0a0
+  - libzlib >=1.3.2,<2.0a0
+  - lz4-c >=1.10.0,<1.11.0a0
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+  - snappy >=1.2.2,<1.3.0a0
+  - zstd >=1.5.7,<1.6.0a0
+  constrains:
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+  - arrow-cpp <0.0a0
+  - apache-arrow-proc =*=cpu
+  license: Apache-2.0
+  license_family: APACHE
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-acero-24.0.0-h66151e4_3_cpu.conda
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@@ -22028,6 +26117,26 @@ packages:
   license_family: APACHE
   size: 545520
   timestamp: 1779974343525
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-acero-25.0.0-h620650e_3_cpu.conda
+  build_number: 3
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+  - libarrow-compute 25.0.0 h03721a0_3_cpu
+  - libcxx >=21
+  - libopentelemetry-cpp >=1.27.0,<1.28.0a0
+  - libprotobuf >=7.35.1,<7.35.2.0a0
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports:
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-compute-24.0.0-h5d4fa73_3_cpu.conda
   build_number: 3
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@@ -22047,6 +26156,28 @@ packages:
   license_family: APACHE
   size: 2386439
   timestamp: 1779973820529
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-compute-25.0.0-h03721a0_3_cpu.conda
+  build_number: 3
+  sha256: d67ef6169267a2704bf0ba6ffe0c3d025ba863865b5a2f3ef04a5e994798d41c
+  md5: 976f7cadd33186f19c215c16745076f5
+  depends:
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+  - libabseil * cxx17*
+  - libabseil >=20260526.0,<20260527.0a0
+  - libarrow 25.0.0 h5ff653a_3_cpu
+  - libcxx >=21
+  - libopentelemetry-cpp >=1.27.0,<1.28.0a0
+  - libprotobuf >=7.35.1,<7.35.2.0a0
+  - libre2-11 >=2025.11.5
+  - libutf8proc >=2.11.3,<2.12.0a0
+  - re2
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports:
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+    - libarrow-compute >=25.0.0,<25.1.0a0
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-dataset-24.0.0-h66151e4_3_cpu.conda
   build_number: 3
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@@ -22066,6 +26197,28 @@ packages:
   license_family: APACHE
   size: 535734
   timestamp: 1779974702285
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-dataset-25.0.0-h620650e_3_cpu.conda
+  build_number: 3
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+  md5: dd087997d423c68a1e2790831f3b7bc4
+  depends:
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+  - libabseil * cxx17*
+  - libabseil >=20260526.0,<20260527.0a0
+  - libarrow 25.0.0 h5ff653a_3_cpu
+  - libarrow-acero 25.0.0 h620650e_3_cpu
+  - libarrow-compute 25.0.0 h03721a0_3_cpu
+  - libcxx >=21
+  - libopentelemetry-cpp >=1.27.0,<1.28.0a0
+  - libparquet 25.0.0 hd9337e7_3_cpu
+  - libprotobuf >=7.35.1,<7.35.2.0a0
+  license: Apache-2.0
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-substrait-24.0.0-h613493e_3_cpu.conda
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@@ -22083,6 +26236,26 @@ packages:
   license_family: APACHE
   size: 449313
   timestamp: 1779974833488
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-substrait-25.0.0-h0ec1645_3_cpu.conda
+  build_number: 3
+  sha256: f8f849c02dd22c175de2a3f1aa2eefe5895dc19272c205fddcadd871c982a32e
+  md5: 2f4b5ce9ec2837c2c15e0f400b687f88
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+  - libabseil >=20260526.0,<20260527.0a0
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+  - libarrow-acero 25.0.0 h620650e_3_cpu
+  - libarrow-dataset 25.0.0 h620650e_3_cpu
+  - libcxx >=21
+  - libprotobuf >=7.35.1,<7.35.2.0a0
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports:
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.11.0-8_he492b99_openblas.conda
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@@ -22107,6 +26280,9 @@ packages:
   - __osx >=10.13
   license: MIT
   license_family: MIT
+  run_exports:
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+    - libbrotlicommon >=1.2.0,<1.3.0a0
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   timestamp: 1764017554982
 - conda: https://conda.anaconda.org/conda-forge/osx-64/libbrotlidec-1.2.0-h8616949_1.conda
@@ -22117,6 +26293,9 @@ packages:
   - libbrotlicommon 1.2.0 h8616949_1
   license: MIT
   license_family: MIT
+  run_exports:
+    weak:
+    - libbrotlidec >=1.2.0,<1.3.0a0
   size: 30835
   timestamp: 1764017584474
 - conda: https://conda.anaconda.org/conda-forge/osx-64/libbrotlienc-1.2.0-h8616949_1.conda
@@ -22127,6 +26306,9 @@ packages:
   - libbrotlicommon 1.2.0 h8616949_1
   license: MIT
   license_family: MIT
+  run_exports:
+    weak:
+    - libbrotlienc >=1.2.0,<1.3.0a0
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   timestamp: 1764017609985
 - conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.11.0-8_h9b27e0a_openblas.conda
@@ -22150,6 +26332,9 @@ packages:
   - libcxx >=11.1.0
   license: BSD-3-Clause
   license_family: BSD
+  run_exports:
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+    - libcrc32c >=1.1.2,<1.2.0a0
   size: 20128
   timestamp: 1633683906221
 - conda: https://conda.anaconda.org/conda-forge/osx-64/libcurl-8.20.0-h8f0b9e4_0.conda
@@ -22167,6 +26352,25 @@ packages:
   license_family: MIT
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   timestamp: 1777462238769
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libcurl-8.21.0-h06afde3_2.conda
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+  - libssh2 >=1.11.1,<2.0a0
+  - libzlib >=1.3.2,<2.0a0
+  - openssl >=3.5.7,<4.0a0
+  - zstd >=1.5.7,<1.6.0a0
+  license: curl
+  license_family: MIT
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-22.1.6-h19cb2f5_0.conda
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@@ -22192,6 +26396,9 @@ packages:
   md5: 899db79329439820b7e8f8de41bca902
   license: BSD-2-Clause
   license_family: BSD
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/libevent-2.1.12-ha90c15b_1.conda
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   license: BSD-3-Clause
   license_family: BSD
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/libexpat-2.8.1-hcc62823_0.conda
@@ -22276,6 +26486,28 @@ packages:
   license_family: Apache
   size: 1866947
   timestamp: 1779227772698
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libgoogle-cloud-3.7.0-he806f76_0.conda
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+  md5: a907ff8020238b360f5cd72694dbf3d7
+  depends:
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+  - libabseil * cxx17*
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+  - libcurl >=8.21.0,<9.0a0
+  - libcxx >=19
+  - libgrpc >=1.82.1,<1.83.0a0
+  - libopentelemetry-cpp >=1.27.0,<1.28.0a0
+  - libprotobuf >=7.35.1,<7.35.2.0a0
+  - openssl >=3.5.7,<4.0a0
+  constrains:
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+  license: Apache-2.0
+  license_family: Apache
+  run_exports:
+    weak:
+    - libgoogle-cloud >=3.7.0,<3.8.0a0
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/libgoogle-cloud-storage-3.5.0-hea209c6_0.conda
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@@ -22292,6 +26524,25 @@ packages:
   license_family: Apache
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   timestamp: 1779228289061
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libgoogle-cloud-storage-3.7.0-h1159701_0.conda
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+  - libgoogle-cloud 3.7.0 he806f76_0
+  - libzlib >=1.3.2,<2.0a0
+  - openssl
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/libgrpc-1.78.1-h147dede_0.conda
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@@ -22312,6 +26563,29 @@ packages:
   license_family: APACHE
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   timestamp: 1774015913341
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libgrpc-1.82.1-h00dac5a_0.conda
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+  - libabseil >=20260526.0,<20260527.0a0
+  - libcxx >=19
+  - libprotobuf >=7.35.1,<7.35.2.0a0
+  - libre2-11 >=2025.11.5
+  - libzlib >=1.3.2,<2.0a0
+  - openssl >=3.5.7,<4.0a0
+  - re2
+  constrains:
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+  license: Apache-2.0
+  license_family: APACHE
+  run_exports:
+    weak:
+    - libgrpc >=1.82.1,<1.83.0a0
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/libiconv-1.18-h57a12c2_2.conda
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   md5: 210a85a1119f97ea7887188d176db135
@@ -22375,6 +26649,9 @@ packages:
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   license: MIT
   license_family: MIT
+  run_exports:
+    weak:
+    - libnghttp2 >=1.68.1,<2.0a0
   size: 606749
   timestamp: 1773854765508
 - conda: https://conda.anaconda.org/conda-forge/osx-64/libopenblas-0.3.33-openmp_h9e49c7b_0.conda
@@ -22410,6 +26687,28 @@ packages:
   license_family: APACHE
   size: 602246
   timestamp: 1774001890965
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libopentelemetry-cpp-1.27.0-h3bf6f87_1.conda
+  sha256: 91a9e5649c50abb041f35ab18256c8808ac1e5c04f04fb1c35bf643e1a30bcf4
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+  - libgrpc >=1.82.0,<1.83.0a0
+  - libopentelemetry-cpp-headers 1.27.0 h694c41f_1
+  - libprotobuf >=7.35.1,<7.35.2.0a0
+  - libzlib >=1.3.2,<2.0a0
+  - nlohmann_json
+  - prometheus-cpp >=1.3.0,<1.4.0a0
+  constrains:
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+  license: Apache-2.0
+  license_family: APACHE
+  run_exports:
+    weak:
+    - libopentelemetry-cpp >=1.27.0,<1.28.0a0
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@@ -25322,6 +30280,23 @@ packages:
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+  - abseil-cpp =20260526.0
+  license: Apache-2.0
+  license_family: Apache
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-25.0.0-h3cd3611_3_cpu.conda
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+  - libarrow-compute 25.0.0 h93a2d87_3_cpu
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+  - libcxx >=21
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+  - libprotobuf >=7.35.1,<7.35.2.0a0
+  - libre2-11 >=2025.11.5
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-25.0.0-heaff1e6_3_cpu.conda
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+  - libarrow-acero 25.0.0 heaff1e6_3_cpu
+  - libarrow-compute 25.0.0 h93a2d87_3_cpu
+  - libcxx >=21
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlidec-1.2.0-hc919400_1.conda
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   license_family: MIT
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   timestamp: 1780031485452
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   timestamp: 1770863303486
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+  - abseil-cpp =20260526.0
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-  - orc >=2.3.0,<2.3.1.0a0
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   constrains:
-  - apache-arrow-proc =*=cpu
-  - parquet-cpp <0.0a0
   - arrow-cpp <0.0a0
+  - parquet-cpp <0.0a0
+  - apache-arrow-proc =*=cpu
   license: Apache-2.0
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+  - apache-arrow-proc * cpu
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports: {}
+  size: 3761042
+  timestamp: 1784258836073
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-25.0.0-py312h12c7521_0_cpu.conda
+  sha256: 1fc4c2777ca2363a9dfeb4ed1123706a5a9fcae903584f04f1ad75723df0ace8
+  md5: 53cffbf212097b8333f1baeb1e13ead9
+  depends:
+  - libarrow 25.0.0.* *cpu
+  - libarrow-compute 25.0.0.* *cpu
+  - libzlib >=1.3.2,<2.0a0
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  constrains:
+  - libprotobuf >=6.33.5
+  - numpy >=1.23,<3
+  - apache-arrow-proc * cpu
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports: {}
+  size: 3649324
+  timestamp: 1784279006824
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-25.0.0-py313hc76bb52_0_cpu.conda
+  sha256: cb368ff79a4e7fc1b89f9f2902056fd2b4ccecac787e0dc27fbdffd365ff4d3c
+  md5: ce4c745809c6510984265ab4775866fb
+  depends:
+  - libarrow 25.0.0.* *cpu
+  - libarrow-compute 25.0.0.* *cpu
+  - libzlib >=1.3.2,<2.0a0
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  constrains:
+  - libprotobuf >=6.33.5
+  - apache-arrow-proc * cpu
+  - numpy >=1.23,<3
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports: {}
+  size: 3680813
+  timestamp: 1784258807433
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-25.0.0-py314h159fc0c_0_cpu.conda
+  sha256: 2e0422f3b9cc1aa067638cccb0a8e917809550a91eb781cfbf64d5a7e2b8e7ab
+  md5: 54155aba0ef1fe72c52022617e453d52
+  depends:
+  - libarrow 25.0.0.* *cpu
+  - libarrow-compute 25.0.0.* *cpu
+  - libzlib >=1.3.2,<2.0a0
+  - python >=3.14,<3.15.0a0
+  - python_abi 3.14.* *_cp314
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  constrains:
+  - apache-arrow-proc * cpu
+  - numpy >=1.23,<3
+  - libprotobuf >=6.33.5
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports: {}
+  size: 3689897
+  timestamp: 1784258892556
 - conda: https://conda.anaconda.org/conda-forge/win-64/pydantic-core-2.46.4-py310h034784e_0.conda
   sha256: d6d849410667ba1c48857779ebe18e68ec27f3c23d135c6aaa32ac3917906aff
   md5: 1e83b6a712136b7024fac0c001494a8b
@@ -30712,6 +37179,19 @@ packages:
   license_family: BSD
   size: 220305
   timestamp: 1768190225351
+- conda: https://conda.anaconda.org/conda-forge/win-64/re2-2025.11.05-haef9524_2.conda
+  sha256: eb11c0e948c2576378a8e7f22768a967044d54bfbdea9ef358d31074b91c21f0
+  md5: 5d51319ef836277672d665c2fc04bab0
+  depends:
+  - libre2-11 2025.11.05 h45f70d9_2
+  license: BSD-3-Clause
+  license_family: BSD
+  run_exports:
+    weak:
+    - libre2-11 >=2025.11.5
+    - re2
+  size: 222198
+  timestamp: 1781312702688
 - conda: https://conda.anaconda.org/conda-forge/win-64/rpds-py-0.30.0-py310h034784e_0.conda
   sha256: a9176da0165e1fdc0582945ec22cbfac03c1bb88120389c7fe0b7406b5fee08f
   md5: f2ae7538b9ab9a7cd375fc23e320c2b0
@@ -31000,6 +37480,9 @@ packages:
   - ucrt >=10.0.20348.0
   license: BSD-3-Clause
   license_family: BSD
+  run_exports:
+    weak:
+    - snappy >=1.2.2,<1.3.0a0
   size: 67417
   timestamp: 1762948090450
 - conda: https://conda.anaconda.org/conda-forge/win-64/sqlalchemy-2.0.50-py314hc5dbbe4_0.conda
@@ -31127,6 +37610,7 @@ packages:
   - vc14_runtime >=14.51.36231
   license: BSD-3-Clause
   license_family: BSD
+  run_exports: {}
   size: 20170
   timestamp: 1780005880423
 - conda: https://conda.anaconda.org/conda-forge/win-64/winpty-0.4.3-4.tar.bz2
@@ -31312,6 +37796,9 @@ packages:
   - vc14_runtime >=14.44.35208
   license: Zlib
   license_family: Other
+  run_exports:
+    weak:
+    - libzlib >=1.3.2,<2.0a0
   size: 850351
   timestamp: 1774072891049
 - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda
diff --git a/pixi.toml b/pixi.toml
index 610e5628..94c4acf0 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -47,6 +47,7 @@ moto = "*"
 mypy = ">=1.13"
 pandas = "*"
 pandas-stubs = "*"
+pyarrow = "*"
 pyodbc = "*"
 pytest = "*"
 pytest-benchmark = "*"
@@ -64,7 +65,6 @@ test-no-optional = "pytest -m 'not with_optionals'"
 
 [feature.optionals.dependencies]
 deltalake = "*"
-pyarrow = "*"
 pydantic = ">=2.11.9,<3"
 pydantic-core = ">=2.33.2,<3"
 sqlalchemy = ">=2"
diff --git a/pyproject.toml b/pyproject.toml
index 4279d9c3..0c6fa01b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,7 +31,6 @@ version = "0.0.0"
 
 [project.optional-dependencies]
 deltalake = ["deltalake"]
-pyarrow = ["pyarrow"]
 pydantic = ["pydantic>=2"]
 s3 = ["s3fs>=2025.9"]
 sqlalchemy = ["sqlalchemy"]
@@ -97,7 +96,7 @@ warn_unused_ignores = true
 
 [[tool.mypy.overrides]]
 ignore_missing_imports = true
-module = ["boto3.*", "fsspec.*", "pyarrow.*", "pytest_benchmark.*", "sklearn.*"]
+module = ["boto3.*", "fsspec.*", "pytest_benchmark.*", "sklearn.*"]
 
 [tool.typos.default.extend-identifiers]
 plain_serializer_function_ser_schema = "plain_serializer_function_ser_schema"
diff --git a/tests/column_types/test_object.py b/tests/column_types/test_object.py
index 321e2e89..62451660 100644
--- a/tests/column_types/test_object.py
+++ b/tests/column_types/test_object.py
@@ -43,14 +43,6 @@ def test_validate_dtype(column: Column, dtype: pl.DataType, is_valid: bool) -> N
     assert column.validate_dtype(dtype) == is_valid
 
 
-def test_pyarrow_dtype_raises() -> None:
-    column = dy.Object()
-    with pytest.raises(
-        NotImplementedError, match="PyArrow column cannot have 'Object' type."
-    ):
-        column.pyarrow_dtype
-
-
 def test_sampling_raises() -> None:
     column = dy.Object()
     with pytest.raises(
diff --git a/tests/columns/test_arrow_pycapsule.py b/tests/columns/test_arrow_pycapsule.py
index ae71a3c1..aa6e0136 100644
--- a/tests/columns/test_arrow_pycapsule.py
+++ b/tests/columns/test_arrow_pycapsule.py
@@ -1,6 +1,7 @@
 # Copyright (c) QuantCo 2025-2026
 # SPDX-License-Identifier: BSD-3-Clause
 
+import pyarrow as pa
 import pytest
 
 import dataframely as dy
@@ -12,10 +13,6 @@
     create_schema,
 )
 
-pa = pytest.importorskip("pyarrow")
-
-pytestmark = pytest.mark.with_optionals
-
 
 @pytest.mark.parametrize(
     "column", [dy.Categorical(nullable=True), dy.Enum(["a", "b"], nullable=True)]
diff --git a/tests/columns/test_pyarrow.py b/tests/columns/test_pyarrow.py
deleted file mode 100644
index d3238567..00000000
--- a/tests/columns/test_pyarrow.py
+++ /dev/null
@@ -1,276 +0,0 @@
-# Copyright (c) QuantCo 2025-2026
-# SPDX-License-Identifier: BSD-3-Clause
-
-from typing import TypeVar
-
-import pytest
-from polars._typing import TimeUnit
-
-import dataframely as dy
-from dataframely.columns import Column
-from dataframely.testing import (
-    ALL_COLUMN_TYPES,
-    COLUMN_TYPES,
-    NO_VALIDATION_COLUMN_TYPES,
-    SUPERTYPE_COLUMN_TYPES,
-    create_schema,
-)
-
-pytestmark = pytest.mark.with_optionals
-
-
-T = TypeVar("T", bound=dy.Column)
-
-
-def _nullable(column_type: type[T]) -> T:
-    # dy.Any doesn't have the `nullable` parameter.
-    if column_type == dy.Any:
-        return column_type()
-    return column_type(nullable=True)
-
-
-@pytest.mark.parametrize("column_type", ALL_COLUMN_TYPES)
-def test_equal_to_polars_schema(column_type: type[Column]) -> None:
-    schema = create_schema("test", {"a": _nullable(column_type)})
-    actual = schema.to_pyarrow_schema()
-    expected = schema.create_empty().to_arrow().schema
-    assert actual == expected
-
-
-@pytest.mark.parametrize(
-    "categories",
-    [
-        ("a", "b"),
-        tuple(str(i) for i in range(2**8 - 2)),
-        tuple(str(i) for i in range(2**8 - 1)),
-        tuple(str(i) for i in range(2**8)),
-        tuple(str(i) for i in range(2**16 - 2)),
-        tuple(str(i) for i in range(2**16 - 1)),
-        tuple(str(i) for i in range(2**16)),
-        tuple(str(i) for i in range(2**17)),
-    ],
-)
-def test_equal_polars_schema_enum(categories: list[str]) -> None:
-    schema = create_schema("test", {"a": dy.Enum(categories, nullable=True)})
-    actual = schema.to_pyarrow_schema()
-    expected = schema.create_empty().to_arrow().schema
-    assert actual == expected
-
-
-@pytest.mark.parametrize(
-    "inner",
-    [_nullable(c) for c in ALL_COLUMN_TYPES]
-    + [dy.List(_nullable(t), nullable=True) for t in ALL_COLUMN_TYPES]
-    + [dy.Array(_nullable(t), 1, nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.Struct({"a": _nullable(t)}, nullable=True) for t in ALL_COLUMN_TYPES],
-)
-def test_equal_polars_schema_list(inner: Column) -> None:
-    schema = create_schema("test", {"a": dy.List(inner, nullable=True)})
-    actual = schema.to_pyarrow_schema()
-    expected = schema.create_empty().to_arrow().schema
-    assert actual == expected
-
-
-@pytest.mark.parametrize(
-    "inner",
-    [_nullable(c) for c in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.List(_nullable(t), nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.Array(_nullable(t), 1, nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [
-        dy.Struct({"a": _nullable(t)}, nullable=True)
-        for t in NO_VALIDATION_COLUMN_TYPES
-    ],
-)
-@pytest.mark.parametrize(
-    "shape",
-    [
-        1,
-        0,
-        (0, 0),
-    ],
-)
-def test_equal_polars_schema_array(inner: Column, shape: int | tuple[int, ...]) -> None:
-    schema = create_schema("test", {"a": dy.Array(inner, shape, nullable=True)})
-    actual = schema.to_pyarrow_schema()
-    expected = schema.create_empty().to_arrow().schema
-    assert actual == expected
-
-
-@pytest.mark.parametrize(
-    "inner",
-    [_nullable(c) for c in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.List(_nullable(t), nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.Array(_nullable(t), 1, nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [
-        dy.Struct({"a": _nullable(t)}, nullable=True)
-        for t in NO_VALIDATION_COLUMN_TYPES
-    ],
-)
-def test_equal_polars_schema_struct(inner: Column) -> None:
-    schema = create_schema("test", {"a": dy.Struct({"a": inner}, nullable=True)})
-    actual = schema.to_pyarrow_schema()
-    expected = schema.create_empty().to_arrow().schema
-    assert actual == expected
-
-
-@pytest.mark.parametrize("column_type", COLUMN_TYPES + SUPERTYPE_COLUMN_TYPES)
-@pytest.mark.parametrize("nullable", [True, False])
-def test_nullability_information(column_type: type[Column], nullable: bool) -> None:
-    schema = create_schema("test", {"a": column_type(nullable=nullable)})
-    assert ("not null" in str(schema.to_pyarrow_schema())) != nullable
-
-
-@pytest.mark.parametrize("nullable", [True, False])
-def test_nullability_information_enum(nullable: bool) -> None:
-    schema = create_schema("test", {"a": dy.Enum(["a", "b"], nullable=nullable)})
-    assert ("not null" in str(schema.to_pyarrow_schema())) != nullable
-
-
-@pytest.mark.parametrize(
-    "inner",
-    [_nullable(c) for c in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.List(_nullable(t), nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.Array(_nullable(t), 1, nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [
-        dy.Struct({"a": _nullable(t)}, nullable=True)
-        for t in NO_VALIDATION_COLUMN_TYPES
-    ],
-)
-@pytest.mark.parametrize("nullable", [True, False])
-def test_nullability_information_list(inner: Column, nullable: bool) -> None:
-    schema = create_schema("test", {"a": dy.List(inner, nullable=nullable)})
-    assert ("not null" in str(schema.to_pyarrow_schema())) != nullable
-
-
-@pytest.mark.parametrize(
-    "inner",
-    [_nullable(c) for c in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.List(_nullable(t), nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.Array(_nullable(t), 1, nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [
-        dy.Struct({"a": _nullable(t)}, nullable=True)
-        for t in NO_VALIDATION_COLUMN_TYPES
-    ],
-)
-@pytest.mark.parametrize("nullable", [True, False])
-def test_nullability_information_struct(inner: Column, nullable: bool) -> None:
-    schema = create_schema("test", {"a": dy.Struct({"a": inner}, nullable=nullable)})
-    assert ("not null" in str(schema.to_pyarrow_schema())) != nullable
-
-
-@pytest.mark.parametrize("column_type", COLUMN_TYPES)
-@pytest.mark.parametrize("inner_nullable", [True, False])
-def test_inner_nullability_struct(
-    column_type: type[Column], inner_nullable: bool
-) -> None:
-    inner = column_type(nullable=inner_nullable)
-    schema = create_schema("test", {"a": dy.Struct({"a": inner})})
-    pa_schema = schema.to_pyarrow_schema()
-    struct_field = pa_schema.field("a")
-    inner_field = struct_field.type[0]
-    assert inner_field.nullable == inner_nullable
-
-
-@pytest.mark.parametrize("column_type", COLUMN_TYPES)
-@pytest.mark.parametrize("inner_nullable", [True, False])
-def test_inner_nullability_list(
-    column_type: type[Column], inner_nullable: bool
-) -> None:
-    inner = column_type(nullable=inner_nullable)
-    schema = create_schema("test", {"a": dy.List(inner)})
-    pa_schema = schema.to_pyarrow_schema()
-    list_field = pa_schema.field("a")
-    inner_field = list_field.type.value_field
-    assert inner_field.nullable == inner_nullable
-
-
-def test_nested_struct_in_list_preserves_nullability() -> None:
-    """Test that nested struct fields in lists preserve nullability."""
-    schema = create_schema(
-        "test",
-        {
-            "a": dy.List(
-                dy.Struct(
-                    {
-                        "required": dy.String(nullable=False),
-                        "optional": dy.String(nullable=True),
-                    },
-                    nullable=True,
-                ),
-                nullable=True,
-            )
-        },
-    )
-    pa_schema = schema.to_pyarrow_schema()
-    list_field = pa_schema.field("a")
-    struct_type = list_field.type.value_field.type
-    assert not struct_type[0].nullable
-    assert struct_type[1].nullable
-
-
-def test_nested_list_in_struct_preserves_nullability() -> None:
-    """Test that nested list fields in structs preserve nullability."""
-    schema = create_schema(
-        "test",
-        {
-            "a": dy.Struct(
-                {"list_field": dy.List(dy.String(nullable=False), nullable=True)},
-                nullable=True,
-            )
-        },
-    )
-    pa_schema = schema.to_pyarrow_schema()
-    struct_field = pa_schema.field("a")
-    list_type = struct_field.type[0].type
-    assert not list_type.value_field.nullable
-
-
-def test_deeply_nested_nullability() -> None:
-    schema = create_schema(
-        "test",
-        {
-            "a": dy.Struct(
-                {
-                    "nested": dy.Struct(
-                        {
-                            "required": dy.String(nullable=False),
-                            "optional": dy.String(nullable=True),
-                        },
-                        nullable=True,
-                    ),
-                },
-                nullable=True,
-            )
-        },
-    )
-    pa_schema = schema.to_pyarrow_schema()
-    outer_struct = pa_schema.field("a").type
-    inner_struct = outer_struct[0].type
-    assert not inner_struct[0].nullable  # required field
-    assert inner_struct[1].nullable  # optional field
-
-
-def test_multiple_columns() -> None:
-    schema = create_schema(
-        "test", {"a": dy.Int32(nullable=False), "b": dy.Integer(nullable=True)}
-    )
-    assert str(schema.to_pyarrow_schema()).split("\n") == [
-        "a: int32 not null",
-        "b: int64",
-    ]
-
-
-@pytest.mark.parametrize("time_unit", ["ns", "us", "ms"])
-def test_datetime_time_unit(time_unit: TimeUnit) -> None:
-    schema = create_schema(
-        "test", {"a": dy.Datetime(time_unit=time_unit, nullable=True)}
-    )
-    assert str(schema.to_pyarrow_schema()) == f"a: timestamp[{time_unit}]"
-
-
-@pytest.mark.parametrize("time_unit", ["ns", "us", "ms"])
-def test_duration_time_unit(time_unit: TimeUnit) -> None:
-    schema = create_schema(
-        "test", {"a": dy.Duration(time_unit=time_unit, nullable=True)}
-    )
-    assert str(schema.to_pyarrow_schema()) == f"a: duration[{time_unit}]"