Upgrade to Spark 4.0 to prepare RC

[Deependra-Patel]

Also changed dependencies to be compatible with Dataproc s8s 3.0 runtime

[gemini-code-assist]

Summary of Changes

Hello @Deependra-Patel, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request prepares the project for its "1.0.0-rc" release by performing a significant upgrade to Spark 4.0 and ensuring full compatibility with the Dataproc s8s 3.0 runtime environment. This involves updating the supported Python version to 3.12 across the project, revising numerous dependency versions, and refining the PySpark installation requirements for better user experience and modularity.

Highlights

• Spark 4.0 Upgrade: The project has been upgraded to support Spark 4.0, aligning with the latest Spark release and preparing for a release candidate.
• Python 3.12 Adoption: The required Python version for the project, including documentation builds and development environments, has been updated to Python 3.12.
• Dependency Updates: Several core dependencies, such as google-cloud-aiplatform, pandas, pyarrow, and tensorflow, have been updated to newer versions to ensure compatibility with Dataproc s8s 3.0 runtime.
• Release Candidate Versioning: The project's version has been incremented to "1.0.0-rc", indicating preparation for an upcoming release candidate.
• PySpark Installation Check: A runtime check has been introduced in the main init.py file to ensure PySpark is installed, providing helpful instructions if it's missing.
• Optional PySpark Dependencies: pyspark and pyspark-client are now managed as optional dependencies (via 'spark' and 'spark-connect' extras) rather than core dependencies, offering more flexible installation.

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[gemini-code-assist]

Code Review

This pull request successfully upgrades the project to support Spark 4.0 by updating the Python version to 3.12 and refreshing key dependencies for compatibility with the Dataproc s8s 3.0 runtime. A significant and well-executed change is making pyspark an optional dependency, which enhances the library's flexibility. This is cleanly handled with a runtime check in __init__.py that provides clear installation instructions. The changes are consistent across all modified files. My review includes one suggestion for pyproject.toml to improve the clarity of a dependency version specification for better long-term maintainability.

[gemini-code-assist]

For dependency versioning, it's a good practice to be explicit by including the full patch version. While 18.1 is a valid version specifier, using 18.1.0 is clearer and can prevent potential ambiguity with some package management tools. This small change improves the long-term maintainability and readability of the dependency list.

Suggested change

	"pyarrow>=18.1, <20.0.0",
	"pyarrow>=18.1.0, <20.0.0",