Google BigQuery Cost Optimization for Raw Data Dumps

This repository provides strategies and SQL examples for optimizing Google BigQuery (GBQ) costs when dealing with raw data dumps.

Problem

Directly ingesting raw data into GBQ without proper optimization can lead to excessive query costs. GBQ charges based on the amount of data scanned, so inefficient queries can quickly become very expensive.

Solution

This repository offers practical SQL-based solutions and best practices for cost-effective data analysis in GBQ, including:

• Partitioning and Clustering: Organizing data for efficient querying.
• Limiting Scanned Data: Writing queries that minimize the amount of data processed.
• Optimized Views and Materialized Views: Creating pre-computed results for faster and cheaper queries.

Repository Structure

• README.md: This file.
• sql/optimization_techniques/: Contains SQL scripts demonstrating various optimization techniques.
• sql/example_queries/: Contains example SQL queries for common data analysis scenarios.
• python/: Contains Python scripts for data pre-processing or automation.
• data/: Contains example datasets.

Getting Started

1. Clone this repository.
2. Explore the SQL scripts in the sql/ directory.
3. Adapt the examples to your own GBQ datasets.

Contributing

If you have any suggestions or improvements, please feel free to submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.