Question 1

What's the difference between BigQuery and a traditional relational database (like Cloud SQL or PostgreSQL)?

Accepted Answer

BigQuery is designed for analytics (OLAP): scanning and aggregating very large datasets to answer questions like trends, funnels, and cohorts. Traditional relational databases are designed for transactions (OLTP): lots of small reads/writes like user logins, orders, and inventory updates. BigQuery is optimized for large, read-heavy analytical queries, while Cloud SQL/PostgreSQL is optimized for frequent updates and low-latency transactional workloads.

Question 2

When should I use BigQuery?

Accepted Answer

Use BigQuery when you need to analyze large amounts of data with SQL—such as clickstream analysis, business intelligence reporting, log analytics, marketing attribution, or data science feature generation—without managing servers. It’s a good fit when data is too large or queries are too complex for spreadsheets or a transactional database, and when you want elastic scaling for periodic heavy queries.

Question 3

How much does BigQuery cost?

Accepted Answer

BigQuery pricing typically includes storage and compute. Storage is charged based on how much data you store (with different rates for active vs long-term storage). Compute is usually charged either by data processed per query (on-demand) or via capacity-based pricing (reservations/slots) for predictable workloads. Costs are influenced by how much data your queries scan, how often you run queries, use of features like materialized views, and data ingestion/streaming. Using partitioning, clustering, and selecting only needed columns can reduce query costs.

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