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Glossary · Hosting & DevOps

BigQuery

Definition: BigQuery is Google Cloud's fully managed, serverless data warehouse that runs fast SQL queries over terabytes to petabytes of data, separating storage from compute so teams can analyze large datasets without managing infrastructure.

Official source: BigQuery

Overview

What BigQuery is

BigQuery is Google Cloud's fully managed, serverless data warehouse for analytics. You load data and query it with standard SQL, and Google handles the infrastructure, with no servers or clusters to provision. It separates storage from compute, so you pay for data stored and for the queries you run independently, and it can scan terabytes to petabytes in seconds.

How it works and what it adds

BigQuery stores data in a columnar format and runs queries across many machines in parallel, which is why large aggregations are fast. On top of SQL, it offers BigQuery ML for building models in SQL, streaming ingestion for near-real-time data, and native connections to tools like Looker Studio and Google Sheets. Pricing is usually per data scanned, on-demand, or via reserved capacity.

Why it matters for marketing and SEO analytics

For analytics and attribution work, BigQuery is where scattered data comes together: ad platforms, CRM, GA4 exports, and call tracking in one place you can query and join. In technical SEO, it makes log files and large crawl or Search Console datasets analyzable at a scale spreadsheets cannot handle. That unified layer is what powers reliable marketing attribution instead of siloed, per-channel numbers.

Where we use it

Related Zen in Tech services

How our team puts BigQuery to work in real projects.

FAQ

BigQuery — common questions

Is BigQuery free?

BigQuery has a free tier each month, currently 1 TB of query processing and 10 GB of storage, which is enough for small projects and learning. Beyond that you pay for data scanned by queries and for storage, so cost depends on data volume and query patterns.

BigQuery vs. a traditional database?

A transactional database like PostgreSQL or MySQL is built for fast reads and writes of individual records powering an app. BigQuery is an analytical warehouse built to scan and aggregate huge volumes of data for reporting and analysis, not to run a live application.

Do I need BigQuery for marketing analytics?

You need it once your data outgrows spreadsheets or you want to combine multiple sources, such as GA4, ads, CRM, and offline conversions, into one queryable dataset. For a single small site with light traffic, GA4 and Looker Studio alone are usually enough.

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