Feat: Support pandas in BigQuery cache

[jscheel]

Uses the pandas_gpb library to support fetching BigQuery tables to pandas DataFrames.

Summary by CodeRabbit

• New Features

  • Added support for reading BigQuery data using Pandas.
  • Introduced a new method for converting SQL tables to Pandas DataFrames.

• Dependencies

  • Added pandas-gbq package to project dependencies.

• Improvements

  • Enhanced data handling methods in cache classes.
  • Streamlined dataset conversion in integration tests.

• Bug Fixes

  • Removed cache type-specific conditional checks in test functions.

[coderabbitai[bot]]

📝 Walkthrough

Walkthrough

This pull request introduces modifications to the caching and data reading mechanisms in the Airbyte project. The changes primarily focus on enhancing the data retrieval process for different cache types, with a specific emphasis on BigQuery integration. The modifications include adding a new private method for reading SQL tables into Pandas DataFrames, updating import statements, and adding a new dependency to support BigQuery data handling.

Changes

File	Change Summary
airbyte/caches/base.py	- Added _read_to_pandas_dataframe method - Updated paired_destination_config method signature
airbyte/caches/bigquery.py	- Replaced get_arrow_dataset with _read_to_pandas_dataframe - Updated import statements
pyproject.toml	- Added pandas-gbq dependency (version >=0.26.1)
tests/integration_tests/cloud/test_cloud_sql_reads.py tests/integration_tests/test_all_cache_types.py	- Simplified dataset conversion tests - Removed conditional checks in test functions

Possibly related PRs

• airbytehq/PyAirbyte#281: Changes in data reading methods for Pandas DataFrames.
• airbytehq/PyAirbyte#407: SQL execution method additions related to _read_to_pandas_dataframe.
• airbytehq/PyAirbyte#298: Introduction of _read_to_pandas_dataframe in the BigQueryCache class.

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Suggested Reviewers

• aaronsteers

Hey there! 👋 I noticed some interesting changes in the caching and data retrieval mechanisms. The new _read_to_pandas_dataframe method looks like it'll streamline how we handle data across different cache types. Would you be interested in discussing the rationale behind moving from get_arrow_dataset to this new approach? Wdyt? 🤔

The addition of pandas-gbq seems to be a key enabler for the BigQuery integration. Are there any specific performance or compatibility improvements you're aiming for with this change? Always curious to hear the backstory! 🚀

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• pyproject.toml
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[aaronsteers]

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