What Is Google BigQuery?

Google BigQuery is a data warehouse. Its job is to store all your marketing data in one place so you can query it, analyze it, and act on it.

If you run ads on Meta, Google Ads, or TikTok, track website traffic in Google Analytics 4, manage leads in HubSpot, and process orders in Shopify, all of that data currently lives in separate platforms. BigQuery brings it together.

What a Data Warehouse Actually Does

Think of a physical warehouse. It is a building where you store products. You have shelves organized by category, and when you need something, a storekeeper retrieves it for you.

BigQuery works the same way. The warehouse is BigQuery itself. The shelves are tables. Each table holds data from one source: advertising, CRM, sales, analytics. And the storekeeper is SQL, the query language you use to request data.

When you write a SQL query, it goes into BigQuery, finds the right tables, and returns exactly what you asked for.

What Data You Can Store in BigQuery

BigQuery stores any structured data. For marketing teams, that typically includes:

• Advertising data: spend, impressions, clicks, conversions, ROAS from Meta Ads, Google Ads, TikTok Ads, LinkedIn Ads.
• CRM data: leads, pipeline stages, deal values, customer lifecycle from HubSpot or Salesforce.
• Sales data: revenue, orders, average order value from Shopify or other e-commerce platforms.
• Analytics data: sessions, users, traffic sources, goal completions from Google Analytics 4.

Once all of this is in BigQuery, you stop analyzing each platform in isolation. You query everything together.

How BigQuery Fits Into Your Marketing Stack

BigQuery sits at the center of your marketing stack. Data flows in from your ad platforms, your CRM, and your analytics tools. You query that data using SQL. Then you send the results to a reporting tool like Looker Studio, or to a spreadsheet, or to any other tool your team uses.

This setup gives you one source of truth for your marketing performance. Every report, every dashboard, every decision pulls from the same data.

What You Can Do With BigQuery as a Marketer

Here are four things marketing teams do with BigQuery that they cannot do when their data lives in separate platforms:

1. Combine ad spend across platforms. You query Meta, Google Ads, TikTok, and LinkedIn in a single table. You see total spend, total conversions, and blended ROAS without switching between platforms.
2. Connect ad performance to revenue. You join your ad data with your Shopify data. You see which campaigns drove actual purchases, not just clicks.
3. Segment audiences. You combine CRM data with ad performance data to understand which customer segments respond to which campaigns.
4. Trigger automations. You use BigQuery data to feed automated actions in other tools, like pausing campaigns that exceed a cost-per-lead threshold.

Why Marketing Teams Choose BigQuery Over Other Options

BigQuery is fully managed by Google. You do not set up servers, manage infrastructure, or worry about scaling. Google handles all of that.

It connects natively with Google Ads, Google Analytics 4, and Looker Studio. If your team already uses Google’s ecosystem, BigQuery is the natural next step.

The pricing model works for most marketing teams. You get 10GB of storage and 1TB of query processing free every month. Most small and mid-size marketing teams stay within the free tier or pay very little.

How to Get Your Marketing Data Into BigQuery

You need a pipeline to move data from your ad platforms, CRM, and analytics tools into BigQuery. You have two options.

Option 1: Build it yourself. You write scripts or use open-source tools to extract data from each API and load it into BigQuery. This requires engineering resources and ongoing maintenance as APIs change.

Option 2: Use a marketing connector. Porter Metrics connects your marketing data sources to BigQuery automatically. You select your ad accounts, your analytics, and your CRM. Porter extracts, normalizes, and loads the data into BigQuery on a schedule you set.

The connector approach gets you a working marketing data warehouse in minutes, not weeks.

Getting Started With Google BigQuery

To start using BigQuery, you need a Google Cloud account. Google Cloud is the platform that hosts BigQuery. Once you create an account and a project, you enable BigQuery and you are ready to create datasets and tables.

The BigQuery Studio interface has three panels: an explorer on the left where you navigate your datasets and tables, a query editor in the center where you write SQL, and a results panel at the bottom where you see your data.

If you want to connect your marketing data to BigQuery without writing code, Porter Metrics handles the pipeline for you. You connect your first data source and your data starts flowing into BigQuery automatically.