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2 changes: 1 addition & 1 deletion mkdocs/docs/api.md
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Expand Up @@ -1572,7 +1572,7 @@ print(ray_dataset.take(2))

### Daft

PyIceberg interfaces closely with Daft Dataframes (see also: [Daft integration with Iceberg](https://www.getdaft.io/projects/docs/en/stable/integrations/iceberg/)) which provides a full lazily optimized query engine interface on top of PyIceberg tables.
PyIceberg interfaces closely with Daft Dataframes (see also: [Daft integration with Iceberg](https://docs.daft.ai/en/stable/io/iceberg/)) which provides a full lazily optimized query engine interface on top of PyIceberg tables.

<!-- prettier-ignore-start -->

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4 changes: 2 additions & 2 deletions pyproject.toml
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Expand Up @@ -78,7 +78,7 @@ gcsfs = { version = ">=2023.1.0", optional = true }
huggingface-hub = { version = ">=0.24.0", optional = true }
psycopg2-binary = { version = ">=2.9.6", optional = true }
sqlalchemy = { version = "^2.0.18", optional = true }
getdaft = { version = ">=0.2.12", optional = true }
daft = { version = ">=0.5.0", optional = true }
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Updating the minor version requires running poetry lock. What's the rationale for bumping to 0.5.0?

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We changed our package from getdaft to daft and the earliest version for daft starts at v0.4.7 but we wanted to bump to v0.5.0

cachetools = ">=5.5,<7.0"
pyiceberg-core = { version = "^0.5.1", optional = true }
polars = { version = "^1.21.0", optional = true }
Expand Down Expand Up @@ -298,7 +298,7 @@ pyarrow = ["pyarrow", "pyiceberg-core"]
pandas = ["pandas", "pyarrow"]
duckdb = ["duckdb", "pyarrow"]
ray = ["ray", "pyarrow", "pandas"]
daft = ["getdaft"]
daft = ["daft"]
polars = ["polars"]
snappy = ["python-snappy"]
hive = ["thrift"]
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