Quickstart

​

Prerequisites

• A Detrics account with at least one connected platform (Meta Ads, Google Ads, Shopify, etc.)
• A Google Cloud Platform (GCP) project with BigQuery enabled
• Permission to grant BigQuery access in your GCP project

​

Step 1: Create a Destination

1. Go to Data Warehouse → Destinations in the Detrics web app
2. Click New Destination
3. Fill in your destination details:
   • Name — A label for this destination (e.g., “Production Warehouse” or “Marketing Analytics”)
   • GCP Project ID — The project ID from your Google Cloud Console (e.g., my-company-analytics)
   • Dataset Name — The BigQuery dataset where tables will be created (e.g., marketing_data). If the dataset doesn’t exist, Detrics will create it
   • Data Location — Where your data is physically stored (e.g., US, EU, or a specific region like us-east1)
   • Timezone — The default timezone for your transfers
4. Click Create Destination

​

Step 2: Grant BigQuery Access

1. Detrics will show you a service account email (e.g., [email protected])
2. Go to your Google Cloud Console → IAM
3. Click Grant Access
4. Paste the Detrics service account email as the new principal
5. Assign the BigQuery User role
6. Click Save

​

Step 3: Test the Connection

1. Go back to your destination in Detrics
2. Click Test Connection
3. Detrics will verify it can create datasets, create tables, insert data, and query your project
4. Once the test passes, your destination is ready

​

Step 4: Create a Table Group

1. Go to Data Warehouse → Tables
2. Click New Table Group
3. Choose the platform (e.g., Meta Ads, Google Analytics 4, Shopify)
4. You’ll see two options:
   • Start from a preset — Pre-configured table groups with recommended metrics and dimensions. Choose Standard for a good starting point
   • Start from scratch — Build your own tables with exactly the fields you need
5. Give your table group a name (e.g., “Meta Ads - Campaign Performance”)

​

Step 5: Configure Your Tables

1. Click on any table in the group to edit it
2. Select your metrics (e.g., spend, impressions, clicks, conversions) and dimensions (e.g., campaign_name, date, ad_name)
3. Set the time aggregation — how data is grouped over time:
   • Daily — One row per day per dimension combination (most common)
   • Weekly / Monthly — Aggregated by week or month
   • Total — No time breakdown, all-time totals
4. Set the historical sync range — how far back to fetch data on the first sync (e.g., last 3 months, last 12 months, or all time)

​

Step 6: Create a Transfer

1. Go to Data Warehouse → Transfers
2. Click New Transfer
3. Configure the transfer:
   • Data Source — Select the connection (e.g., your Meta Ads connection)
   • Accounts — Choose All accounts or select specific ad accounts, properties, or stores
   • Table Group — Select the table group you created in Step 4
   • Sync Mode — Assign a sync mode to each table in the group (Incremental, Full Refresh, or Full Append)
   • Destination — Select the destination you created in Step 1
   • Schedule — How often to sync (e.g., Daily at 6:00 AM in your timezone)
   • Table Name Prefix — Optional prefix for BigQuery table names (e.g., meta_ads_)
4. Click Create Transfer

​

Step 7: Monitor Your Sync

1. Click on your new transfer to see the detail page
2. The sync progress panel shows real-time progress: which table is loading, how many rows have been processed, and estimated time remaining
3. Once complete, you’ll see a summary with total rows loaded per table

​

Step 8: Query Your Data in BigQuery

your_dataset.meta_ads_campaign_performance
your_dataset.meta_ads_ad_performance

SELECT
  campaign_name,
  SUM(spend) as total_spend,
  SUM(impressions) as total_impressions,
  SUM(link_clicks) as total_clicks,
  ROUND(SUM(spend) / NULLIF(SUM(link_clicks), 0), 2) as cost_per_click
FROM `your-project.your_dataset.meta_ads_campaign_performance`
WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY campaign_name
ORDER BY total_spend DESC

​

What’s Next?