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

-- Standard SQL
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

• BigQuery Documentation