How to Set Up Your BigQuery Project ID, Dataset, and Table Name

After you connect your marketing accounts and authenticate BigQuery in Porter Metrics, the next step is selecting where your data will land. This means choosing your BigQuery Project ID, your dataset location, your dataset name, and your table name.

Here is what each setting means and how to configure it correctly.

Step 1: Select Your Project ID

Your Project ID is the unique identifier for your Google Cloud project. It looks something like “my-marketing-project-123456.” You find it in the Google Cloud console at the top of the page or in your project settings.

In Porter Metrics, after authenticating BigQuery, you see a dropdown of all projects available under your authenticated Google account. Select the project where you want your marketing data to land.

If you manage multiple clients, you typically have one project per client or one shared project for all clients. Select the appropriate project for this particular data sync.

Step 2: Select Your Dataset Location

Dataset location is the geographic region where your data is stored in BigQuery. The most common options are:

US (multi-region): stores data across multiple data centers in the United States. The default for most teams in North America.

EU (multi-region): stores data across multiple data centers in Europe. Required for teams with data residency requirements under GDPR.

Specific regions: you select a single region such as us-central1 or europe-west1 for more precise control.

Choose the location closest to where your team works. Once you create a dataset in a location, you cannot move it to a different location. If you need to change the location, you create a new dataset and migrate your data.

Step 3: Name Your Dataset

Your dataset name is the container that holds your BigQuery tables. Use a clear, descriptive name that reflects what data it contains.

Good dataset names for marketing data:

meta_ads

google_ads

ga4

paid_media_all_platforms

client_name_marketing

Dataset names must use only lowercase letters, numbers, and underscores. No spaces, no hyphens, no special characters.

Porter Metrics creates the dataset automatically if it does not already exist. If you type a dataset name that already exists in your project, Porter uses the existing dataset.

Step 4: Name Your Table

Your table name is the specific table inside your dataset where your data lands. Use a name that describes the data in the table.

Good table names:

daily_performance

campaign_level_data

ads_ga4_blended

weekly_summary

Like dataset names, table names use only lowercase letters, numbers, and underscores.

Porter creates the table automatically with the correct schema based on the metrics and dimensions you selected. You do not define the schema manually.

Creating New vs Using Existing Datasets and Tables

When you type a dataset name or table name in Porter, Porter checks whether it already exists in your BigQuery project.

• If the dataset or table does not exist: Porter creates it automatically when you run your first sync.
• If the dataset or table already exists: Porter uses the existing one. Depending on your write mode setting (overwrite, append, or update), Porter either replaces the existing data, adds new rows, or updates existing rows.

This means you can use Porter to add new data to a table that already contains historical data. You do not need to start from scratch each time.

A Practical Example

Here is what a complete BigQuery destination configuration looks like in practice:

• Project ID: my-agency-project-987654
• Dataset location: US
• Dataset name: client_meta_ads
• Table name: daily_campaign_performance

Porter creates the dataset “client_meta_ads” in your project (if it does not exist) and creates the table “daily_campaign_performance” inside it. Every day, Porter loads the previous day’s Meta Ads data into that table.

After a few weeks, your table contains a growing history of daily campaign performance data, ready to query in BigQuery Studio or connect to Looker Studio.