Google BigQuery is a fully managed, serverless data warehouse built for large-scale analytics. For data engineers, it offers a rich set of features spanning pricing models, storage optimization, ingestion pipelines, security controls, transformation frameworks, and operational tooling. This cheat sheet covers every major capability you need to design, build, and operate production BigQuery workloads.
What This Cheat Sheet Covers
This topic spans 16 focused tables and 101 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Pricing Models — On-Demand vs Capacity-Based
BigQuery offers two fundamentally different billing approaches. On-demand charges per byte scanned and suits sporadic workloads, while capacity-based pricing purchases dedicated slots and suits high-throughput, predictable workloads. Choosing the right model has a large impact on total cost.
| Model | Example | Description |
|---|---|---|
-- $6.25 per TiB scanned-- 2,000 concurrent slots per projectSELECT * FROM large_table | • Charges $6.25 per TiB of bytes processed • no upfront commitment • default model for new projects | |
-- Standard: $0.04/slot-hr-- Enterprise: $0.06/slot-hr-- Enterprise Plus: $0.10/slot-hr | • Slot-hour billing replaces per-byte charges • choose Standard, Enterprise, or Enterprise Plus based on feature needs and SLO requirements | |
-- Max 1,600 baseline slots-- Autoscale only (no baseline)-- $0.04/slot-hr | • Entry-level capacity edition • supports autoscaling but not baseline slot reservations • no cross-region disaster recovery | |
-- Baseline + autoscale slots-- BigQuery Omni supported-- $0.06/slot-hr | Adds baseline slot reservations, multi-region replication, BigQuery Omni, and 99.9% SLO over Standard. |