bigquery-advanced-utils 0.0.1.dev6

A utility library that enhances the official BigQuery Python client by providing additional tools for efficient query management, data processing, and automation. Designed for developers and data scientists, `bigquery-advanced-utils` simplifies working with Google BigQuery.

bigquery-advanced-utils

BigQuery-advanced-utils is a lightweight utility library that extends the official Google BigQuery Python client.
It simplifies tasks like query management, data processing, and automation.

Aimed at developers and data scientists, the project is open to contributions to improve and enhance its functionality.

For full documentation, please visit: BigQuery-Advanced-Utils Documentation

Why This Library?

I created bigquery-advanced-utils because I often found myself facing complex or uncommon tasks when working with BigQuery, and there was little or no support available online. Rather than spending time reinventing the wheel, I decided to create this library to help others avoid the same challenges. I hope that everyone can contribute in the same spirit, so feel free to get involved and make this library even better!

Requirements

• Python 3.10+

Installation 📦

Install via pip (recommended)

Run the following command in your terminal:

pip install bigquery-advanced-utils

Install in a Virtual Environment

1. Create a virtual environment:

python -m venv venv

2. Activate the environment and install:

source venv/bin/activate  # on macOS/Linux  
venv\Scripts\activate     # on Windows  
pip install bigquery-advanced-utils

Usage Examples 🚀

Quick Start

from bigquery_advanced_utils.bigquery import BigQueryClient

# Initialize helper with your project ID
helper = BigQueryClient(project_id="your_project_id")

# Load data from CSV
helper.load_data_from_csv(file_path="your_data.csv")

Data Validation

test_functions=[
    partial(
        # Check if any null values exist in the "age" column
        b.check_no_nulls, columns_to_test=["age"],
    ),
    partial(
        # Ensure values in the "email" column are unique
        b.check_unique_column, columns_to_test=["email"]
    )
]

Search by table or owner in Datatransfer

from bigquery_advanced_utils.datatransfer import DataTransferClient

helper = DataTransferClient()
# Call the function with two parameters: owner email and project id
list_transfer_config_by_owner_email(owner_email="[e-mail]", project_id="my-project")

# Get the scheduled queries by the name of a table (it's case sensitive) 
list_transfer_configs_by_table(table_id="my-table", project_id="my-project")

Planned features 🚧

• A new query builder.
• Custom data transformation and processing functions.
• Exclusive features with datatransfer.
• Utility functions to manipulate strings and query.

Contributing

We are always open to contributions! Whether you have a bug fix, a feature request, or a general improvement to make, your help is appreciated. Here are some ways you can contribute:

• Bug reports: Help us catch issues before they affect users.
• New features: Suggest new functionalities that could improve the usability of the package.
• Code improvements: Review the code and suggest optimizations or fixes.

Please follow the contributing guide for more details on how to get started.

License

This project is licensed under the GNU General Public License. See the LICENSE file for details.

Contact

For questions or feedback, feel free to open an issue or reach out to me.