v0.4.2 Polars, GCS, and distributed lances

A warm welcome to @hzhang86 as Lance's newest contributor. Thanks for adding TPCH benchmarks for Lance to establish a baseline. This is really helpful for us to focus performance optimization roadmap.

This release is packed with valuable features:

1. Direct polars scan without needing to pull everything into memory is added.
2. We expose FileFragment's to allow distributed processing engines like Spark to access parts of a Lance dataset easily
3. Last but not least, we've added support for reading Lance data directly from GS buckets

What's Changed

• [Rust] FileReader read range API by @eddyxu in #752
• Support direct polars scan by @changhiskhan in #755
• [Rust] Persist graph using lance file format. by @eddyxu in #756
• Refactor PQ and OPQ training function to make it usable widely by @eddyxu in #758
• Matrix::centroids method by @eddyxu in #759
• [Python] Set minimal version of Polars for python tests by @eddyxu in #765
• [Rust] Refactor RecordBatchStream trait by @eddyxu in #766
• [Rust] Expose DataFragment as pubilc dataset api. by @eddyxu in #769
• Revert "[Python] Set minimal version of Polars for python tests (#765)" by @gsilvestrin in #770
• add python script to compare lance performance vs parquet TPCH by @hzhang86 in #749
• Expose index metadata by @changhiskhan in #768
• Google Cloud Storage support. by @gsilvestrin in #773
• [Python] Expose DataFragment via dataset by @eddyxu in #774
• Get S3 credentials from_env by @changhiskhan in #775
• Fix duckdb build by @eddyxu in #776
• [Rust] A arrow kernel to compute hash value of the array. by @eddyxu in #777

New Contributors

• @hzhang86 made their first contribution in #749

Full Changelog: v0.4.1...v0.4.2