In this tutorial, we’ll show you how to send your Meta Ads data to Google BigQuery with Porter. We’ll send campaign performance data including fields like Campaign Name, Impressions, Clicks, and custom fields like CPA and campaign segmentation by funnel stage.

Set a connection

Log in to portermetrics.com with Google. Click “Create” and select ” Google BigQuery ” as destination. Name your connection (e.g., “Meta Ads Campaign Performance”). Select Meta Ads as data source.

• Data blending : optionally, add Google Ads , GA4, Shopify in the same connection for cross-channel reports.

Connect your Meta Ads accounts

Connect your Facebook profile and grant access to pages and ad accounts. Select “Current and Future Business Pages and Accounts” to avoid re-authorization in the future. Select the Ad Accounts you want to connect.

Connect your BigQuery destination

Authenticate with Google login or Service Account. Select Project ID, Dataset location, Dataset, and Table name.

• Google login (recommended) : Porter lists your projects in a dropdown. Easiest option.

• Service Account JSON : for companies with strict permissions management on Google Workspace. Copy a JSON text from your project details to connect.

• Dataset location : US, EU, or your preferred region.

• Auto-update schema : if you change your query later, Porter updates the schema automatically and rewrites it in your BigQuery table, unlike other tools.

New to BigQuery? Create your first project:

Go to console.cloud.google.com . In the Navigation Menu (top left), select BigQuery → Studio. On the left panel, you’ll see your projects.

1. Create a Project : select or create a new project (e.g., “Marketing Data”). Choose a name, type, and organization. BigQuery creates a folder for it.

2. Create a Dataset : expand your project folder, click the ellipsis, and select “Create Dataset.” Name it (e.g., “ppc_data”) and select a location (US or EU).

3. Create a Table : inside your dataset, you can create a table (e.g., “meta_ads”). Or let Porter create it automatically when you send your first query.

The Project ID, Dataset name, and Table name you set here are the same values you’ll enter in Porter’s BigQuery configuration.

Verify your data in BigQuery:

When you select a table, BigQuery shows the Schema view first. This is the metadata: field names, field types, and modes. To see your exported data, go to the Preview tab. Once your query executes, you’ll see the complete table with your data.

Choose metrics

In the metrics dropdown, search and select: e.g., Amount Spent, Purchases, Leads, Custom Conversions, Impressions, Link Clicks.

• 800+ Fields Available

  Porter brings most fields and constantly updates from the Ads Manager. Expect the exact same data you see in Meta.

  See all Meta Ads fields

• All Attribution Windows

  Compare conversions across 1-day click, 7-day click, 28-day click, 1-day view, 7-day view in one query.

• Lead Ads Data

  Bring form fields and submitted information from users directly to your data warehouse.

Choose dimensions

To segment your data, in the dimensions dropdown, search and select: e.g., Campaign Name, Date.

• Other dimensions : Ad Account, Objective, Ad Set Name, Ad Name, Creative (image URL, video URL, headline, primary text, ad preview link), Hour, Week, Month, Year.

• Breakdowns : age, gender, device, placement, platform, region/country.

• Instagram Ads included : use placement or platform breakdown to separate Instagram from Facebook.

• Some combinations restricted by Meta : e.g., conversions + age + gender + placement. You can’t combine multiple breakdowns with conversions in a query. If unavailable, simplify or create separate queries.

See all Meta Ads fields

Create custom fields

For custom metrics , add a new metric, prompt your formula in natural language, and check the formula generated and a preview of the query. Choose the format of your metric (number, currency, percentage). For this example: CPC = “spend / clicks”, CPA = “spend / conversions”.

For custom dimensions , prompt your formula to segment data based on naming conventions. If your naming conventions include objective, funnel stage, or products, prompt a formula like: “If campaign name contains ‘awareness’, tag as ‘Awareness’. If contains ‘consideration’, tag as ‘Consideration’. If contains ‘conversion’, tag as ‘Conversion’. Else ‘Other’.” In the preview, see how Porter transforms conditionals into regex for custom segmentations.

Create your own metrics or dimensions so no SQL or transformation is needed in BigQuery. Your data is ready to be connected to Looker Studio. Supported operations: math (sum, subtract, divide, multiply), conditionals (if/then/else), regex (pattern matching). Same capabilities as Looker Studio calculated fields.

Set date range

Select a date range from the dropdown. For this example: last 30 days.

• Dynamic ranges : today, yesterday, last 7/14/28/30/90 days, this week/month/quarter/year to date, last week/month/quarter/year.

• Fixed ranges : specific start and end dates.

• Auto-update : data refreshes automatically based on dynamic range.

Add filters

The Meta API recently started returning campaigns with no activity. When you run a query, you get campaigns with spend and campaigns with zero spend, which is noise to your tables. We’ll create a filter to exclude them.

For this example:

• Condition : Exclude

• Field : Amount Spent

• Operator : equals

• Value : 0

This excludes all campaigns without activity, so your query only returns campaigns with spend.

• Available operators : equals, contains, not contains, greater than, less than, starts with, ends with.

• Value detection : Porter detects if the field is a number or text automatically.

• Combine filters : add AND/OR logic within the same condition or create multiple filters in one query.

Schedule refresh

Prompt your schedule in natural language. For this example: “every day at 8am”.

• Examples : “Every Monday at 5am”, “Weekdays at 7pm”, “Every hour”, “Every Tuesday and Friday at 9am”.

• Auto-convert : Porter converts prompts into cron expressions.

• Timezone : detected automatically from your browser.

• Minimum frequency : every minute. No extra cost for frequent refreshes.

Choose write mode

Select how Porter writes data to BigQuery. For this example: Overwrite.

• Overwrite (recommended) : deletes existing table and writes fresh data. No duplicates.

• Append : adds new rows below existing data. Risk of duplicates if same date range runs twice.

• Update : matches rows by dimension and updates values. For CRM data with changing values.

Send, monitor, and organize

Click “Save” to save your query and click “Send” to deliver the data to Google BigQuery. The transfer takes a few seconds depending on the volume of data. Once finished, you can refresh it or create more queries.

To create more queries: go back to the query manager inside your connection, or go to Porter Metrics → Account → Reports → Connections. In the Queries tab, you’ll see all queries running from your account with their associated connection, name, data sources, last run time, latest status, and option to run manually.

To monitor executions: click the ellipses icon and select “History.” You’ll see logs with exact date and time, execution type (manual or scheduled), and status. If an error occurs, you’ll see the specific error message.

To organize your data: manage connections and queries within them. Name connections by campaign (e.g., “Black Friday”), by client, or by data source. Within each connection, create as many queries or tables as needed and rename them. You can enable/disable queries or connections, and update any query anytime—Porter refreshes and updates the schema on BigQuery automatically.