BigQuery changed the rules of the game in the data-warehouse world. You don't manage servers, storage and compute scale separately, and petabyte-scale queries complete in minutes. Anyone who knows classic SQL is set to start.
01. Serverless Architecture
There's no server management in BigQuery. You just write the query; data storage and processing are decoupled. This is the fundamental difference from traditional MPP databases.
02. Storage and Compute Separation
The money you pay for storage is low ($20/TB/month), and what you pay for queries is on-demand. The two components scale independently.
03. SQL and ML
It works with standard ANSI SQL. With BigQuery ML you can train an ML model within SQL: with the CREATE MODEL ... statement, linear regression, K-means, deep learning.
04. Pricing Models
Two payment paths:
- On-demand: $6.25/TB per query (scanned)
- Flat-rate: monthly slot capacity (predictable cost)
Large analytics teams switch to flat-rate.
05. Enterprise Use
BigQuery is becoming widespread in growing e-commerce and finance companies in Turkey. Together with Power BI and Looker Studio, it can be the foundation of an enterprise analytics infrastructure.