| name | bigquery |
| description | Google BigQuery for analytics, ML, and data warehousing. Use for large-scale analytics. |
Google BigQuery
BigQuery is Google's serverless, highly scalable, and cost-effective multi-cloud data warehouse. It processes terabytes in seconds.
When to Use
- Serverless Analytics: No infrastructure to manage. Just run SQL.
- Real-time Analytics: High-speed streaming ingestion.
- ML Integration:
CREATE MODEL lets you train ML models using standard SQL (BigQuery ML).
Quick Start
SELECT name, COUNT(*) as count
FROM `bigquery-public-data.usa_names.usa_1910_2013`
GROUP BY name
ORDER BY count DESC
LIMIT 10;
Core Concepts
Slots and Reservations
A "Slot" is a unit of computational capacity. BigQuery autoscales slots, or you can reserve them for flat-rate pricing.
Columnar Storage (Capacitor)
Optimized for aggregation queries. Reading one column is much cheaper/faster than reading all columns (SELECT * is expensive).
Partitioning & Clustering
- Partitioning: Splits table by Date/Int (e.g., Daily partitions). Prunes data scanning massive cost savings.
- Clustering: Sorts data within partitions for faster filtering.
Best Practices (2025)
Do:
- Partition by Date: Almost mandatory for time-series logs.
- Use BigQuery ML: Train models (Regression, K-Means) directly where data lives.
- Estimate Cost:
Dry Run your query to see how many bytes it will scan before running it.
Don't:
- Don't run
SELECT *: You pay per column read. Select only what you need.
- Don't treat it like an OLTP: Single row inserts are slow (unless using Streaming API). It is for bulk analytics.
References
